Most Singapore firms experienced data breach, worried over 5G deployments

A majority 96% of businesses in Singapore admit to experiencing a data breach over the past year, while 98% have expressed security concerns about digital transformation initiatives and 5G deployments. Amongst those that have such worries, 55% believe these will facilitate more "destructive" cybercrime activities while 54% say they will create more opportunities for cyber attacks. 

Another 34% believed 5G and digital transformation would lead to a lack of visibility over their organisation's network, revealed a survey commissioned by security vendor Carbon Black. Conducted by research firm Opinion Matters, the study polled more than 250 chief information and security officers in Singapore, across various verticals including financial, healthcare, government, and retail. 

According to the survey, 93% of local businesses saw an increase in cyber attacks in the past 12 months and 92% described such incidents to be more sophisticated. 

Some 93% of respondents also noted an increase in attack frequency, with most of these in the government and food and beverage sectors. In addition, 33% of manufacturing and engineering companies as well as 54% of healthcare providers reported an increase of between 25% and 50% over the past year. Another 56% of financial services institutions reported a 51% to 100% increase in the frequency of cyber attacks. 

Just below half, 42% said they experienced a breach once, while 34% reported at least five security breaches. Fifteen respondents had been breached at least 10 times. 

However, the average number of breaches reported by Singapore respondents dropped to 3.82, compared to 3.98 clocked in Carbon Black's January 2019 report. Government and local authority agencies, though, saw a high average number of breaches at 7.06 per year. 

Amongst those that experienced a security breach, 48% of Singapore companies admitted incurring financial damage but 9% declined to describe the financial impact on their company. Another 83% acknowledged there was damage to their brand reputation after reporting a breach, including 60% who described they suffered "some to severe" negative effects following a breach. This figure was a higher 75% amongst government organisations as well as 67% of engineering and manufacturing companies. 

The study identified ransomware attacks as the most successful, accounting for 29% of breaches in Singapore, followed by phishing and web application attacks, which contributed 19% and 9% of successful breaches, respectively.

Despite the high volume of cyber attacks, 84.5% of organisations here expressed more confidence in their ability to thwart such attempts now than they were a year ago. Some 95% pointed to threat hunting as a reason for their improved defenses, including 40.5% that described a "significant protective effect". 

Almost all, at 99%, Singapore companies planned to increase their security budgets over the next year. 

However, they would find it challenging to recruit and train security specialists, with 67% of respondents describing such efforts as tougher compared to a year ago. Recruitment, in particular, was found to be a lot more difficult by 44% of financial services companies, while 19% of government organisations agreed. 

Carbon Black's head of security strategy Rick McElroy said: "It appears businesses are adjusting to the 'new normal' of sustained and sophisticated cyberattacks. Greater awareness of external threats and compliance risks have also prompted businesses to become more proactive about managing cyber risks as they witness the financial and reputational impacts that breaches entail."

Pointing to the anxiety around mission critical initiatives such as 5G deployments, McElroy added: "A larger attack surface and greater dependency on digital infrastructure means the risks of malicious attack are amplified, and businesses are concerned this will mean more opportunities for cybercrime and the development of more effective and destructive methods.

"There is concern these emerging threats will require bigger security teams drawn from a talent pool that is small, and subject to intense competition, as more organisations compete for limited resources," he said, noting that this would push businesses to adopt tools such as artificial intelligence and automation to improve the visibility of their networks. 

RELATED COVERAGE

Singapore sees drop in common security threats, but foresees more data breaches

Cyber Security Agency says the number of common cyber threats, including website defacements and phishing, dipped in Singapore last year, but expects to see more frequent data breaches and disruptive attacks against the cloud in the near future.

Singapore updates guidelines on data breach notification and accountability

Expected to be included as part of the upcoming amendment to the country's data protection law, the new guidelines state businesses must take no more than 30 days to investigate a suspected breach and notify authorities 72 hours after completing their assessment of the breach.

SingHealth breach review recommends remedies that should already be basic security policies

The review committee also finds IT staff to be lacking in cybersecurity awareness and resources and SingHealth's network misconfigured with security vulnerabilities, which helped hackers succeed in breaching its systems.

Key takeaways from Singapore healthcare data breach

No system is infallible and cybersecurity breaches are inevitable, but Singapore needs to do better in mitigating the risks and following through on its pledge to safeguard citizen data.

Singapore Airlines data breach affects 285 accounts, exposes travel details

Singapore carrier points to "a software bug" as the cause of the breach that occurred when changes were made to its website, compromising personal data of 285 customers including seven whose passport details were exposed.

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Act, Australia reaches 100% renewable electricity target

In the week that sees the Canberra Raiders heading to their first rugby league grand final in 25 years, it is Canberra’s other green machines that have achieved their own milestone, with the national capital now officially 100% powered by renewable electricity.

The ACT has become the first Australian state or territory – with the exception of hydro-rich Tasmania – to source the equivalent of all its electricity from renewable sources, and in doing so has reduced its greenhouse gas emissions by around 40 per cent and given a significant boost to the Canberra economy.

In line with the contracted awarded via a reverse auction to identify the cheapest sources of renewable electricity, ACT is now purchasing the electricity produced by the Hornsdale Stage 3 wind farm in South Australia. The supply from the 109MW wind farm has now tipped the ACT over the 100% renewable electricity milestone.

By shifting all of the ACT’s electricity to renewable sources, mostly wind in NSW, South Australia and Victoria, but also some solar located in and around the Canberra region, the ACT has been able to reduce its greenhouse gas emissions by around 40 per cent, making a substantial contribution towards the territory’s goal of reaching zero net emissions by 2045.

The ACT is now turning its attention to other emission sources, recently unveiling plans to go electric on transport and on buildings, including a proposal to shift homes and businesses away from gas and towards electricity. For that, and to deal with a growing population, the ACT is about to tender for another 250MW of wind and solar, including battery storage.

“This is a huge achievement in consolidating the ACT’s reputation as Australia’s renewable energy capital and is a significant milestone in achieving our ultimate goal of zero net emissions by 2045,” ACT energy minister Shane Rattenbury said.

“The Hornsdale 3 windfarm is the last of our 10 large scale renewable energy generators that are spread across the country and are helping to power the ACT on clean energy at low prices.”

Analysis of the electricity price data released by the ACT government has shown that the contractual arrangements with the renewable energy projects have helped to generate electricity cost savings for Canberra households.

These savings amounted to $4.3 million in the first three months of 2019, as the contracts effectively shielded Canberra energy users from surging wholesale electricity prices.

Source: ACT Government

In reaching the milestone, the ACT becomes the first major jurisdiction in Australia to achieve 100% renewable electricity, with an analysis from The Australia Institute finding that Canberra will become the first major city outside of Europe to switch its power supply from one supplied primarily by fossil fuels to one powered completely by wind and solar.

“While some federal parliamentarians are trying to hit the brakes Australia’s energy transition, even Parliament House will soon run on 100% renewable energy,” The Australia Institute’s climate and energy program director Richie Merzian pointed out.

“This shows that states and territories are leading the way on climate action while national governments often lag behind. Australia is a perfect example.”

Earlier in September, the ACT government confirmed that it will seek proposals to supply up to an additional 250MW of renewable energy capacity to the ACT, as the territory seeks to maintain its status of being 100% renewable powered as it looks to shift more of its energy use, including gas and transport, on to the electricity grid.

This additional capacity will also be paired with large-scale battery storage, with a 20MW/40MWh battery storage system to be co-located with the new project.

“With our recently announced new renewable electricity auction, we are now in a position where we are ready to maintain 100% renewable electricity from 2020 into the future. Reaching this feat has proven that climate change action is both achievable and affordable,” Rattenbury added.

“Our four renewable electricity auctions have also helped bring in more than $500 million worth of investment into the ACT region and help make us a centre for high-skilled renewable sector jobs.”

Through the arrangements with renewable energy developers under the 100% renewable electricity plan, the ACT government has also been successful at attracting companies to relocate their headquarters, or at least part of their Australian-based operations, to Canberra.

Companies including Windlab and Neoen have each established offices in the national capital and strong renewables investment has supported local firms, such as ITP Renewables and virtual power plant provider Reposit Power, grow their base in the ACT providing additional economic benefits to the territory.

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Zeigarnik Effect

In psychology, the Zeigarnik effect states that people remember uncompleted or interrupted tasks better than completed tasks. In Gestalt psychology, the Zeigarnik effect has been used to demonstrate the general presence of Gestalt phenomena: not just appearing as perceptual effects, but also present in cognition.^[1]

Overview

Russian psychologist Bluma Zeigarnik first studied the phenomenon after her professor, Gestalt psychologist Kurt Lewin, noticed that a waiter had better recollections of still unpaid orders. However, after the completion of the task – after everyone had paid – he was unable to remember any more details of the orders. Zeigarnik then designed a series of experiments to uncover the processes underlying this phenomenon. Her research report was published in 1927.^[2]

The advantage of remembrance can be explained by looking at Lewin's field theory: a task that has already been started establishes a task-specific tension, which improves cognitive accessibility of the relevant contents.^[3] The tension is relieved upon completion of the task. If the task is interrupted, the reduction of tension is impeded. Through continuous tension, the content is made more easily accessible, and can be easily remembered.^[3]

The Zeigarnik effect suggests that students who suspend their study to perform unrelated activities (such as studying a different subject or playing a game), will remember material better than students who complete study sessions without a break (McKinney 1935; Zeigarnik 1927).

Ovsiankina effect

The Zeigarnik effect should not be confused with the Ovsiankina effect. Maria Ovsiankina was a colleague of Bluma Zeigarnik who investigated the effect of task interruption on the tendency to resume the task at the next opportunity.^[4]

Harden rule

The Zeigarnik effect has been used to explain the widespread criticism of the National Basketball Association in allowing free throws for a player "chucking it up whenever a guy comes near them." There is a stoppage of play with each foul. When repeatedly done, it is felt to build up a cognitive bias against this move. The criticism necessitated a rule change penalizing this activity, known as the Harden Rule, named after its most prominent user, James Harden.^[5][6][7]

Criticism

The reliability of the effect has been a matter of some controversy.^[8]

Several later studies, performed later in other countries, attempting to replicate Zeigarnik's experiment, failed to find any significant differences in recall between "finished" and "unfinished" (interrupted) tasks (e.g. Van Bergen, 1968).

See also

1. ^ cf. Kurt Koffka, Principles of Gestalt Psychology, 1935, pp 334ff.
2. ^ Zeigarnik 1927: Das Behalten erledigter und unerledigter Handlungen. Psychologische Forschung 9, 1-85.
3. ^ ^{a b} Kurt Lewin, A Dynamic Theory of Personality, 1935, pp 243ff
4. ^ Ovsiankina 1928: Die Wiederaufnahme unterbrochener Handlungen. In: Psychologische Forschung 11(3/4), 302–379.
5. ^ Boone, Kyle (September 22, 2017). "The NBA is finally cracking down on James Harden's foul-drawing antics". CBSSports.com. Retrieved October 4, 2017.
6. ^ "NBA implementing 'Zaza Pachulia,' 'James Harden' rules". NBCSports.com. September 21, 2017. Retrieved October 4, 2017.
7. ^ https://www.cbssports.com/nba/news/how-the-nbas-newly-imposed-harden-rule-will-impact-james-harden-this-season/Haren rule impact
8. ^ Einstein, Gilles O.; McDaniel, Mark A.; Williford, Carrie L.; Pagan, Jason L.; Dismukes, R. Key (2003). "Forgetting of intentions in demanding situations is rapid" (PDF). Journal of Experimental Psychology: Applied. 9 (3): 147–162. doi:10.1037/1076-898X.9.3.147. PMID 14570509. [...] there is controversy regarding the reliability of the Zeigarnik effect [...]

Further reading

Zeigarnik

• Zeigarnik, B. (1927). Das Behalten erledigter und unerledigter Handlungen. Psychologische Forschung, 9, 1-85.

• Burke W.W., "A Perspective on the Field of Organization Development and Change: The Zeigarnik Effect", The Journal of Applied Behavioral Science, Vol.47, No.2, (June 2011), pp.143-167.
• Denmark, Florence L. (2010). "Zeigarnik Effect". In Weiner, Irving B.; Craighead, W. Edward (eds.). The Corsini Encyclopedia of Psychology (4th ed.). John Wiley & Sons. pp. 1873–1874. doi:10.1002/9780470479216.corpsy0924. ISBN 9780470170236.
• Mazur, Elena, "The Zeigarnik Effect and the Concept of Unfinished Business in Gestalt Therapy", British Gestalt Journal, Vol.5, No.1, (1996), pp.18-23.
• McKinney, Fred, "Studies in the Retention of Interrupted Learning Activities", Journal of Comparative Psychology, Vol.19, No.2, (April 1935), 265-296.
• Oyama, Yoshinori, Manalo, Emmanuel & Nakatan, Yoshihide (2018), "The Hemingway effect: How failing to finish a task can have a positive effect on motivation", Thinking Skills and Creativity. doi=10.1016/j.tsc.2018.01.001
• Savitsky, K., Medvec, V.H. & Gilovich, T., "Remembering and Regretting: The Zeigarnik Effect and the Cognitive Availability of Regrettable Actions and Inactions", Personality and Social Psychology Bulletin, Vol.23, No.3, (March 1997), pp.248-257. doi=10.1177/0146167297233004
• Syrek, C.J., Weigelt, O., Peifer, C. & Antoni, C.H., "Zeigarnik’s sleepless nights: How unfinished tasks at the end of the week impair employee sleep on the weekend through rumination", Journal of Occupational Health Psychology, Vol.22, No.2, (April 2017), pp.225-238.
• Van Bergen, Annie, Task Interruption, North-Holland, (Amsterdam), 1968.
• Weiner, B., Johnson, P.B. & Mehrabian, A., "Achievement Motivation and the Recall of Incomplete and Completed Exam Questions", Journal of Educational Psychology, Vol.59, No.3, (June 1968), pp.181-185.

External links

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Processing 40 TB of code from 10M projects with a dedicated server and Go

The command line tool I created Sloc Cloc and Code (scc) (which is now modified and maintained by many other excellent people) counts lines of code, comments and makes a complexity estimate for files inside a directory. The latter is something you need a good sample size to make good use of. The way it works is that it counts branch statements in code. However what does that actually mean? For example “This file has a complexity of 10” is not very useful without some context. To solve this issue I thought I would try to run scc at all the source code I could get my hands on. This would also allow me to see if there are any edge cases I didn’t consider in the tool itself. A brute force Q/A trial by fire.

However if I am going to run it over all that code, which is going to be computationally expensive I may as well try to get some use out of it. As such I decided to record everything as I went and see if I could get something interesting in the end, hence this post.

In short I downloaded and processed a lot of code using scc. The raw numbers include,

• 9,985,051 total repositories
• 9,100,083 repositories with at least 1 identified file
• 884,968 empty repositories (those with no files)
• 58,389,641 files in all repositories
• 40,736,530,379,778 bytes processed (40 TB)
• 1,086,723,618,560 lines identified
• 816,822,273,469 code lines identified
• 124,382,152,510 blank lines identified
• 145,519,192,581 comment lines identified
• 71,884,867,919 complexity count according to scc rules
• 2 new bugs raised in scc

Lets get the elephant out of the room first. It was not 10 million projects as the “click bait” title indicates. I was shy by 15,000 so I rounded up. Please forgive me.

It took about 5 weeks to download and run scc over the collection of repositories saving all of the data. It took just over 49 hours to crunch the 1 TB of JSON and produce the results below.

Quicklinks

Methodology

Since I run searchcode.com I already have a collection of over 7,000,000 projects across git, mercurial, subversion and such. So why not try processing them? Working with git is usually the easiest solution so I ignored mercurial and subversion this time and exported the full list of git projects. Turns out I actually had 12 million git repositories being tracked, and I should probably update the page to reflect that.

So now I have 12 million or so git repositories which I need to download and process.

When you run scc you can choose to have it output the results in JSON optionally saving this file to disk like so scc --format json --output myfile.json main.go the results of which look like the following (done for a single file),

[ { "Blank": 115, "Bytes": 0, "Code": 423, "Comment": 30, "Complexity": 40, "Count": 1, "Files": [ { "Binary": false, "Blank": 115, "Bytes": 20396, "Callback": null, "Code": 423, "Comment": 30, "Complexity": 40, "Content": null, "Extension": "go", "Filename": "main.go", "Hash": null, "Language": "Go", "Lines": 568, "Location": "main.go", "PossibleLanguages": [ "Go" ], "WeightedComplexity": 0 } ], "Lines": 568, "Name": "Go", "WeightedComplexity": 0 } ]

As a larger example here are the results as JSON for the redis project, redis.json. All of the results below come from this output without any supporting additional data.

One thing to keep in mind is that scc generally categories languages based on extension (except where extension is shared such as Verilog and Coq). As such if someone puts a HTML file with a java extension it will be counted as a java file. Usually this isn’t a problem, because why would you ever do that? But of course at scale it is. It is something I discovered later where some files were masquerading as another.

A while back I wrote code to create github badges using scc https://boyter.org/posts/sloc-cloc-code-badges/ and since part of that included caching the results, I modified it slightly to cache the results as JSON in AWS S3.

With the badge code working in AWS using lambda, I took the exported list of projects, wrote about 15 lines of python to clean the format so it matched my lambda and make a request to it. I threw in some python multiprocessing to fork 32 processes to call the endpoint reasonably quickly.

This worked brilliantly. However the problem with the above was firstly the cost, and secondly lambda behind API-Gateway/ALB has a 30 second timeout, so it couldn’t process large repositories fast enough. I knew going in that this was not going to be the most cost effective solution but assuming it came close to $100 I would have been willing to live with it. After processing 1 million repositories I checked and the cost was about $60 and since I didn’t want a $700 AWS bill I decided to rethink my solution. Keep in mind that was mostly storage and CPU, or what was needed to collect this information. Assuming I processed or exported the data it was going to increase the cost considerably.

Since I was already in AWS the hip solution would be to dump the url’s as messages into SQS and pull from it using EC2 instances or fargate for processing. Then scale out like crazy. However despite working in AWS in my day job I have always believed in taco bell programming. Besides it was only 12 million repositories so I opted to implement a simpler (cheaper) solution.

Running this computation locally was out due to the abysmal state of the internet in Australia. However I do run searchcode.com fairly lean using dedicated servers from Hetzner. These boxes are quite powerful, i7 Quad Core 32 GB RAM machines often with 2 TB of disk space (usually unused). As such they usually have a lot of spare compute based on how I use them. The front-end varnish box for instance is doing the square root of zero most of the time. So why not run the processing there?

I didn’t quite taco bell program the solution using bash and gnu tools. What I did was write a simple Go program to spin up 32 go-routines which read from a channel, spawned git and scc subprocesses before writing the JSON output into S3. I actually wrote a Python solution at first, but having to install the pip dependencies on my clean varnish box seemed like a bad idea and it keep breaking in odd ways which I didn’t feel like debugging.

Running this on the box produced the following sort of metrics in htop, and the multiple git/scc processes running (scc is not visible in this screen capture) suggested that everything was working as expected, which I confirmed by looking at the results in S3.

Presenting and Computing Results

Having recently read https://mattwarren.org/2017/10/12/Analysing-C-code-on-GitHub-with-BigQuery/ and https://psuter.net/2019/07/07/z-index I thought I would steal the format of those posts with regards to how I wanted to present the information. My twist on the previous however is to add jQuery DataTables over the large tables of information. This allows you to sort and search/filter results. As such you can click the headers to sort and use the search box to filter. The search box indicates that this is enabled for you. I also added a jump link near these tables so you can skip over them if you like.

The size of the data I needed to process raised another question. How does one process 10 million JSON files taking up just over 1 TB of disk space in an S3 bucket?

The first thought I had was AWS Athena. But since it’s going to cost something like $2.50 USD per query for that dataset I quickly looked for an alternative. That said if you kept the data there and processed it infrequently this might still work out to be the cheapest solution.

I posted the question on the company slack because why should I solve issues alone.

One idea raised was to dump the data into a large SQL database. However this means processing the data into the database, then running queries over it multiple times. Plus the structure of the data meant having a few tables which means foreign keys and indexes to ensure some level of performance. This feels wasteful because we could just process the data as we read it from disk in a single pass. I was also worried about building a database this large. With just data it would be over 1 TB in size before adding indexes.

Seeing as I produced the JSON using spare compute, I thought why not process the results the same way? Of course there is one issue with this. Pulling 1 TB of data out of S3 is going to cost a lot. In the event the program crashes that is going to be annoying. To reduce costs I wanted to pull all the files down locally and save them for further processing. Handy tip, you really do not want to store lots of little files on disk in a single directory. It sucks for runtime performance and file-systems don’t like it.

My answer to this was another simple go program to pull the files down from S3 then store them in a tar file. I could then process that file over and over. The process itself is done though very ugly go program to process the tar file so I could re-run my questions without having to trawl S3 over and over. I didn’t bother with go-routines for this code for two reasons. The first was that I didn’t want to max out my server, so this limits it to a single core for the hard CPU work (another to read the tar file was mostly blocked on the processor). The second being I didn’t want to ensure it was thread-safe.

With that done, what I needed was a collection of questions to answer. I used the slack brains trust again and crowd-sourced my work colleagues while I came up with some ideas of my own. The result of this mind meld is included below.

You can find all the code I used to process the JSON including that which pulled it down locally and the ugly python script I used to mangle it into something useful for this post https://github.com/boyter/scc-data Please don’t comment on it, I know the code is ugly and it is something I wrote as a throwaway as I am unlikely to ever look at it again.

If you do want to review code I have written to be read by others have a look at the source of scc.

Cost

I spent about $60 in compute while trialling lambda. I have not looked at the S3 storage cost yet but it should be close to $25 based on the size of the data. However this is not including the transfer costs which I also have not observed. Please note I cleared the bucket when I was finished with it so this is not an ongoing cost for me.

However after time I chose not to use AWS in the end because of cost. So what’s the real cost assuming I wanted to do it again?

Well to start all the software used is free as in freedom and open source. So nothing to worry about there.

In my case the cost would be free as I used “free compute” left over from searchcode. Not everyone has compute lying around however. So lets assume another person wishes to replicate this and as such needs to get a server.

It could be done for €73 using the cheapest new dedicated server from Hetzner https://www.hetzner.com/dedicated-rootserver However that cost includes a new server setup fee. If you are willing to wait and poke around on their auction house https://www.hetzner.com/sb you can find much cheaper servers with no setup fee at all. At time of writing I found the below machine which would be perfect for this project and is €25.21 a month with no setup fee.

Best part though? You can get the VAT removed if you are outside the EU. So give yourself an additional 10% discount on top if you are in this situation.

So were someone to do this from scratch using the same method I eventually went with it would cost under $100 USD to redo the same calculations, and more likely under $50 if you are a little patient or lucky. This also assumes you use the server for less than 2 months which is enough time to download and process. This also includes enough time for you to get a list of 10 million repositories to consider processing as well.

If I were to use a gzipped tar file in my analysis (which isn’t that hard to do really) I could even do 10x the repositories on the same machine as the resulting file would still be small enough to fit on the same hard disk. That would take longer to download though which is going to increase the cost for each additional month and this might take multiple months to do.

Going much larger then 100 million repositories however is going to require some level of sharding. Still it is safe to say that you could redo the entire process I did or larger one on the same hardware without much effort or code changes.

Data Sources

From the three sources, github, bitbucket and gitlab how many projects came from each? Note that this is counted before excluding empty repositories hence the sum is over the number of repositories that actually form the counts below this point.

source	count
github	9,680,111
bitbucket	248,217
gitlab	56,722

Sorry to the GitHub/Bitbucket/GitLab teams if you read this. If this caused any issues for you (I doubt it) I will shout you a refreshing beverage of your choice should we ever meet.

How many files in a repository?

On to the real questions. Lets start with a simple one. How many files are in an average repository? Do most projects have a few files in them, or many? By looping over the repositories and counting the number of files we can then drop them in buckets of 1, 2, 10, 12 or however many files it has and plot it out.

The X-axis in this case being buckets of the count of files, and Y-axis being the count of projects with that many files. This is limited to projects with less than 1000 files because the plot looks like empty with a thin smear on the left side if you include all the outliers.

As it turns out most repositories have less than 200 files in them.

However what about plotting this by percentile, or more specifically by 95th percentile so its actually worth looking at? Turns out the vast majority 95% of projects have less than 1,000 files in them. While 90% of them have less than 300 files and 85% have less than 200.

If you want to plot this yourself and do a better job than I here is a link to the raw data filesPerProject.json.

Whats the project breakdown per language?

This means for each project scanned if a Java file is identified increment the Java count by one and for the second file do nothing. This gives a quick view of what languages are most commonly used. Unsurprisingly the most common languages include markdown, .gitignore and plain text.

Markdown the most commonly used language in any project is included in just over 6 million projects which is about ²⁄₃ of the entire project set. This makes sense since almost all projects include a README.md which is displayed in HTML for repository pages.

The full list is included below.

skip table to next section

language	project count
Markdown	6,041,849
gitignore	5,471,254
Plain Text	3,553,325
JavaScript	3,408,921
HTML	3,397,596
CSS	3,037,754
License	2,597,330
XML	2,218,846
JSON	1,903,569
YAML	1,860,523
Python	1,424,505
Shell	1,395,199
Ruby	1,386,599
Java	1,319,091
C Header	1,259,519
Makefile	1,215,586
Rakefile	1,006,022
PHP	992,617
Properties File	909,631
SVG	804,946
C	791,773
C++	715,269
Batch	645,442
Sass	535,341
Autoconf	505,347
Objective C	503,932
CoffeeScript	435,133
SQL	413,739
Perl	390,775
C#	380,841
ReStructuredText	356,922
MSBuild	354,212
LESS	281,286
CSV	275,143
C++ Header	199,245
CMake	173,482
Patch	169,078
Assembly	165,587
XML Schema	148,511
m4	147,204
JavaServer Pages	142,605
Vim Script	134,156
Scala	132,454
Objective C++	127,797
Gradle	126,899
Module-Definition	120,181
Bazel	114,842
R	113,770
ASP.NET	111,431
Go Template	111,263
Document Type Definition	109,710
Gherkin Specification	107,187
Smarty Template	106,668
Jade	105,903
Happy	105,631
Emacs Lisp	105,620
Prolog	102,792
Go	99,093
Lua	98,232
BASH	95,931
D	94,400
ActionScript	93,066
TeX	84,841
Powershell	80,347
AWK	79,870
Groovy	75,796
LEX	75,335
nuspec	72,478
sed	70,454
Puppet	67,732
Org	67,703
Clojure	67,145
XAML	65,135
TypeScript	62,556
Systemd	58,197
Haskell	58,162
XCode Config	57,173
Boo	55,318
LaTeX	55,093
Zsh	55,044
Stylus	54,412
Razor	54,102
Handlebars	51,893
Erlang	49,475
HEX	46,442
Protocol Buffers	45,254
Mustache	44,633
ASP	43,114
Extensible Stylesheet Language Transformations	42,664
Twig Template	42,273
Processing	41,277
Dockerfile	39,664
Swig	37,539
LD Script	36,307
FORTRAN Legacy	35,889
Scons	35,373
Scheme	34,982
Alex	34,221
TCL	33,766
Android Interface Definition Language	33,000
Ruby HTML	32,645
Device Tree	31,918
Expect	30,249
Cabal	30,109
Unreal Script	29,113
Pascal	28,439
GLSL	28,417
Intel HEX	27,504
Alloy	27,142
Freemarker Template	26,456
IDL	26,079
Visual Basic for Applications	26,061
Macromedia eXtensible Markup Language	24,949
F#	24,373
Cython	23,858
Jupyter	23,577
Forth	22,108
Visual Basic	21,909
Lisp	21,242
OCaml	20,216
Rust	19,286
Fish	18,079
Monkey C	17,753
Ada	17,253
SAS	17,031
Dart	16,447
TypeScript Typings	16,263
SystemVerilog	15,541
Thrift	15,390
C Shell	14,904
Fragment Shader File	14,572
Vertex Shader File	14,312
QML	13,709
ColdFusion	13,441
Elixir	12,716
Haxe	12,404
Jinja	12,274
JSX	12,194
Specman e	12,071
FORTRAN Modern	11,460
PKGBUILD	11,398
ignore	11,287
Mako	10,846
TOML	10,444
SKILL	10,048
AsciiDoc	9,868
Swift	9,679
BuildStream	9,198
ColdFusion CFScript	8,614
Stata	8,296
Creole	8,030
Basic	7,751
V	7,560
VHDL	7,368
Julia	7,070
ClojureScript	7,018
Closure Template	6,269
AutoHotKey	5,938
Wolfram	5,764
Docker ignore	5,555
Korn Shell	5,541
Arvo	5,364
Coq	5,068
SRecode Template	5,019
Game Maker Language	4,557
Nix	4,216
Vala	4,110
COBOL	3,946
Varnish Configuration	3,882
Kotlin	3,683
Bitbake	3,645
GDScript	3,189
Standard ML (SML)	3,143
Jenkins Buildfile	2,822
Xtend	2,791
ABAP	2,381
Modula3	2,376
Nim	2,273
Verilog	2,013
Elm	1,849
Brainfuck	1,794
Ur/Web	1,741
Opalang	1,367
GN	1,342
TaskPaper	1,330
Ceylon	1,265
Crystal	1,259
Agda	1,182
Vue	1,139
LOLCODE	1,101
Hamlet	1,071
Robot Framework	1,062
MUMPS	940
Emacs Dev Env	937
Cargo Lock	905
Flow9	839
Idris	804
Julius	765
Oz	764
Q#	695
Lucius	627
Meson	617
F*	614
ATS	492
PSL Assertion	483
Bitbucket Pipeline	418
PureScript	370
Report Definition Language	313
Isabelle	296
JAI	286
MQL4	271
Ur/Web Project	261
Alchemist	250
Cassius	213
Softbridge Basic	207
MQL Header	167
JSONL	146
Lean	104
Spice Netlist	100
Madlang	97
Luna	91
Pony	86
MQL5	46
Wren	33
Just	30
QCL	27
Zig	21
SPDX	20
Futhark	16
Dhall	15
FIDL	14
Bosque	14
Janet	13
Game Maker Project	6
Polly	6
Verilog Args File	2

How many files in a repository per language?

An extension of the above, but averaged over however many files are in each language per repository. So for projects that contain java, how many java files exist in that project, and on average for all projects how many files is that?

You can use this to see if a project is larger or smaller than usual for your language of choice.

skip table to next section

language	average file count
ABAP	1.0008927583699165
ASP	1.6565139917314107
ASP.NET	346.88867258489296
ATS	7.888545610390882
AWK	5.098807478952136
ActionScript	15.682562363539644
Ada	7.265376817272021
Agda	1.2669381110755398
Alchemist	7.437307493090622
Alex	20.152479318023637
Alloy	1.0000000894069672
Android Interface Definition Language	3.1133707938643074
Arvo	9.872687772928423
AsciiDoc	14.645389421879814
Assembly	1049.6270518312476
AutoHotKey	1.5361384288472488
Autoconf	33.99728695464163
BASH	3.7384110335355545
Basic	5.103623499110781
Batch	3.943513588378872
Bazel	1.0013122734382187
Bitbake	1.0878349272366024
Bitbucket Pipeline	1
Boo	5.321822367969364
Bosque	1.28173828125
Brainfuck	1.3141119785974242
BuildStream	1.4704635441667189
C	15610.17972307699
C Header	14103.33936083782
C Shell	3.1231084093649315
C#	45.804460355773394
C++	30.416980313492328
C++ Header	8.313450764990089
CMake	37.2566873554469
COBOL	3.129408853490878
CSS	5.332398714337156
CSV	8.370432089241898
Cabal	1.0078125149013983
Cargo Lock	1.0026407549221519
Cassius	4.657169356495984
Ceylon	7.397692655679642
Clojure	8.702303821528872
ClojureScript	5.384518778099244
Closure Template	1.0210028022356945
CoffeeScript	45.40906609668401
ColdFusion	13.611857060674573
ColdFusion CFScript	40.42554202020521
Coq	10.903652047164622
Creole	1.000122070313864
Crystal	3.8729367926098117
Cython	1.9811811237515262
D	529.2562750397005
Dart	1.5259554297822313
Device Tree	586.4119588123021
Dhall	5.072265625
Docker ignore	1.0058596283197403
Dockerfile	1.7570825852789156
Document Type Definition	2.2977520758534693
Elixir	8.916658446524252
Elm	1.6702759813968946
Emacs Dev Env	15.720268315288969
Emacs Lisp	11.378847912292201
Erlang	3.4764894379621607
Expect	2.8863991651091614
Extensible Stylesheet Language Transformations	1.2042068607534995
F#	1.2856606249320954
F*	32.784058919015
FIDL	1.8441162109375
FORTRAN Legacy	11.37801716560221
FORTRAN Modern	27.408192558594685
Fish	1.1282354207617833
Flow9	5.218046229973186
Forth	10.64736177807574
Fragment Shader File	3.648087980622546
Freemarker Template	8.397226930409037
Futhark	4.671875
GDScript	3.6984173692608313
GLSL	1.6749061330076334
GN	1.0193083210608163
Game Maker Language	3.6370866431049604
Game Maker Project	1.625
Gherkin Specification	60.430588516231666
Go	115.23482489228113
Go Template	28.011342078505013
Gradle	5.628880473160033
Groovy	6.697367294187844
HEX	22.477003537989486
HTML	4.822243456786672
Hamlet	50.297887645777536
Handlebars	36.60120978679127
Happy	5.820573911044464
Haskell	8.730027121836951
Haxe	20.00590981880653
IDL	79.38510300176867
Idris	1.524684997890027
Intel HEX	113.25178379632708
Isabelle	1.8903018088753136
JAI	1.4865150753259275
JSON	6.507823973898348
JSONL	1.003931049286678
JSX	4.6359645801363465
Jade	5.353279289700571
Janet	1.0390625
Java	118.86142228014006
JavaScript	140.56079100796154
JavaServer Pages	2.390251418283771
Jenkins Buildfile	1.0000000000582077
Jinja	4.574843152310338
Julia	6.672268339671913
Julius	2.2510109380818903
Jupyter	13.480476117239338
Just	1.736882857978344
Korn Shell	1.5100887455636172
Kotlin	3.9004723322169363
LD Script	16.59996086864524
LESS	39.6484785300563
LEX	5.892075421476933
LOLCODE	1.0381496530137617
LaTeX	5.336103768010524
Lean	1.6653789470747329
License	5.593879701111845
Lisp	33.15947937896521
Lua	24.796117625764612
Lucius	6.5742989471450155
Luna	4.437807061133055
MQL Header	13.515527575704464
MQL4	6.400151428436254
MQL5	46.489316522221515
MSBuild	4.8321384193507875
MUMPS	8.187699062741014
Macromedia eXtensible Markup Language	2.1945287114300807
Madlang	3.7857666909751373
Makefile	1518.1769808494607
Mako	3.410234685769436
Markdown	45.687500000234245
Meson	32.45071679724949
Modula3	1.1610784588847318
Module-Definition	4.9327688042002595
Monkey C	3.035163164383345
Mustache	19.052714578803542
Nim	1.202213335585401
Nix	2.7291879559930488
OCaml	3.7135029841909697
Objective C	4.9795510788040005
Objective C++	2.2285232767506264
Opalang	1.9975597794742732
Org	5.258117805392903
Oz	22.250069644336204
PHP	199.17870638869982
PKGBUILD	7.50632295051949
PSL Assertion	3.0736406530442473
Pascal	90.55238627885495
Patch	25.331829692384225
Perl	27.46770444081142
Plain Text	1119.2375825397799
Polly	1
Pony	3.173291031071342
Powershell	6.629884642978543
Processing	9.139907354078636
Prolog	1.816763080890156
Properties File	2.1801967863634255
Protocol Buffers	2.0456253005879304
Puppet	43.424491631161054
PureScript	4.063801504037935
Python	22.473917606983292
Q#	5.712939431518483
QCL	7.590678825974464
QML	1.255201818986247
R	2.3781868952970115
Rakefile	14.856192677576413
Razor	62.79058974450959
ReStructuredText	11.63852408056825
Report Definition Language	23.065085061465403
Robot Framework	2.6260137148703535
Ruby	554.0134362337432
Ruby HTML	24.091116656979562
Rust	2.3002003813895207
SAS	1.0032075758254648
SKILL	1.9229039972029645
SPDX	2.457843780517578
SQL	2.293643752864969
SRecode Template	20.688193360975845
SVG	4.616972531365432
Sass	42.92418345584642
Scala	1.5957851387966393
Scheme	10.480490204644848
Scons	2.1977062552968114
Shell	41.88552208947577
Smarty Template	6.90615223295527
Softbridge Basic	22.218602385698304
Specman e	2.719783829645327
Spice Netlist	2.454830619852739
Standard ML (SML)	3.7598713626650295
Stata	2.832579915520368
Stylus	7.903926412469745
Swift	54.175594149331914
Swig	2.3953681161240747
SystemVerilog	7.120705494624247
Systemd	80.83254275520476
TCL	46.9378307136513
TOML	1.0316491217260413
TaskPaper	1.0005036006351133
TeX	8.690789447558961
Thrift	1.620168483240211
Twig Template	18.33051814392764
TypeScript	1.2610517452930048
TypeScript Typings	2.3638072576034137
Unreal Script	2.9971615019965148
Ur/Web	3.420488425604595
Ur/Web Project	1.8752869585517504
V	1.8780624768245784
VHDL	5.764059992075602
Vala	42.22072166146626
Varnish Configuration	1.9899734258599446
Verilog	1.443359777332832
Verilog Args File	25.5
Vertex Shader File	2.4700152399875077
Vim Script	3.2196359799822662
Visual Basic	119.8397831247842
Visual Basic for Applications	2.5806381264096503
Vue	249.0557418123258
Wolfram	1.462178856761796
Wren	227.4526259500999
XAML	2.6149608174399264
XCode Config	6.979387911493798
XML	146.10128153519918
XML Schema	6.832042266604565
Xtend	2.87054940757827
YAML	6.170148717655746
Zig	1.071681022644043
Zsh	2.6295064863912088
gitignore	6.878908416722053
ignore	1.0210649380633772
m4	57.5969985568356
nuspec	3.245791111381787
sed	1.3985770380241234

How many lines of code are in a typical file per language?

I suppose you could also look at this as what languages on average have the largest files? Using the average/mean for this pushes the results out to stupidly high numbers. This is because projects such as sqlite.c which is included in many projects is joined from many files into one, but nobody ever works on that single large file (I hope!).

So I calculated this using the median value. Even so there are still some definitions with stupidly high numbers such as Bosque and JavaScript.

So I figured why not have both? I did one small change based on the suggestion of Darrell (Kablamo’s resident and most excellent data scientist) and modified the average value to ignore files over 5000 lines to remove the outliers.

skip table to next section

language	mean < 5000	median
ABAP	139	36
ASP	513	170
ASP.NET	315	148
ATS	945	1,411
AWK	431	774
ActionScript	950	2,676
Ada	1,179	13
Agda	466	89
Alchemist	1,040	1,463
Alex	479	204
Alloy	72	66
Android Interface Definition Language	119	190
Arvo	257	1,508
AsciiDoc	519	1,724
Assembly	993	225
AutoHotKey	360	23
Autoconf	495	144
BASH	425	26
Basic	476	847
Batch	178	208
Bazel	226	20
Bitbake	436	10
Bitbucket Pipeline	19	13
Boo	898	924
Bosque	58	199,238
Brainfuck	141	177
BuildStream	1,955	2,384
C	1,052	5,774
C Header	869	126,460
C Shell	128	77
C#	1,215	1,138
C++	1,166	232
C++ Header	838	125
CMake	750	15
COBOL	422	24
CSS	729	103
CSV	411	12
Cabal	116	13
Cargo Lock	814	686
Cassius	124	634
Ceylon	207	15
Clojure	521	19
ClojureScript	504	195
Closure Template	343	75
CoffeeScript	342	168
ColdFusion	686	5
ColdFusion CFScript	1,231	1,829
Coq	560	29,250
Creole	85	20
Crystal	973	119
Cython	853	1,738
D	397	10
Dart	583	500
Device Tree	739	44,002
Dhall	124	99
Docker ignore	10	2
Dockerfile	76	17
Document Type Definition	522	1,202
Elixir	402	192
Elm	438	121
Emacs Dev Env	646	755
Emacs Lisp	653	15
Erlang	930	203
Expect	419	195
Extensible Stylesheet Language Transformations	442	600
F#	384	64
F*	335	65
FIDL	655	1,502
FORTRAN Legacy	277	1,925
FORTRAN Modern	636	244
Fish	168	74
Flow9	368	32
Forth	256	62
Fragment Shader File	309	11
Freemarker Template	522	20
Futhark	175	257
GDScript	401	1
GLSL	380	29
GN	950	8,866
Game Maker Language	710	516
Game Maker Project	1,290	374
Gherkin Specification	516	2,386
Go	780	558
Go Template	411	25,342
Gradle	228	22
Groovy	734	13
HEX	1,002	17,208
HTML	556	1,814
Hamlet	220	70
Handlebars	506	3,162
Happy	1,617	0
Haskell	656	17
Haxe	865	9,607
IDL	386	210
Idris	285	42
Intel HEX	1,256	106,650
Isabelle	792	1,736
JAI	268	41
JSON	289	39
JSONL	43	2
JSX	393	24
Jade	299	192
Janet	508	32
Java	1,165	697
JavaScript	894	73,979
JavaServer Pages	644	924
Jenkins Buildfile	79	6
Jinja	465	3,914
Julia	539	1,031
Julius	113	12
Jupyter	1,361	688
Just	62	72
Korn Shell	427	776
Kotlin	554	169
LD Script	521	439
LESS	1,086	17
LEX	1,014	214
LOLCODE	129	4
LaTeX	895	7,482
Lean	181	9
License	266	20
Lisp	746	1,201
Lua	820	559
Lucius	284	445
Luna	85	48
MQL Header	793	10,337
MQL4	799	3,168
MQL5	384	631
MSBuild	558	160
MUMPS	924	98,191
Macromedia eXtensible Markup Language	500	20
Madlang	368	340
Makefile	309	20
Mako	269	243
Markdown	206	10
Meson	546	205
Modula3	162	17
Module-Definition	489	7
Monkey C	140	28
Mustache	298	8,083
Nim	352	3
Nix	240	78
OCaml	718	68
Objective C	1,111	17,103
Objective C++	903	244
Opalang	151	29
Org	523	24
Oz	360	7,132
PHP	964	14,660
PKGBUILD	131	19
PSL Assertion	149	108
Pascal	1,044	497
Patch	676	12
Perl	762	11
Plain Text	352	841
Polly	12	26
Pony	338	42,488
Powershell	652	199
Processing	800	903
Prolog	282	6
Properties File	184	18
Protocol Buffers	576	8,080
Puppet	499	660
PureScript	598	363
Python	879	258
Q#	475	5,417
QCL	548	3
QML	815	6,067
R	566	20
Rakefile	122	7
Razor	713	1,842
ReStructuredText	735	5,049
Report Definition Language	1,389	34,337
Robot Framework	292	115
Ruby	739	4,942
Ruby HTML	326	192
Rust	1,007	4
SAS	233	65
SKILL	526	123
SPDX	1,242	379
SQL	466	143
SRecode Template	796	534
SVG	796	1,538
Sass	682	14,653
Scala	612	661
Scheme	566	6
Scons	545	6,042
Shell	304	4
Smarty Template	392	15
Softbridge Basic	2,067	3
Specman e	127	0
Spice Netlist	906	1,465
Standard ML (SML)	478	75
Stata	200	12
Stylus	505	214
Swift	683	663
Swig	1,031	4,540
SystemVerilog	563	830
Systemd	127	26
TCL	774	42,396
TOML	100	17
TaskPaper	37	7
TeX	804	905
Thrift	545	329
Twig Template	713	9,907
TypeScript	461	10
TypeScript Typings	1,465	236,866
Unreal Script	795	927
Ur/Web	429	848
Ur/Web Project	33	26
V	704	5,711
VHDL	952	1,452
Vala	603	2
Varnish Configuration	203	77
Verilog	198	2
Verilog Args File	456	481
Vertex Shader File	168	74
Vim Script	555	25
Visual Basic	738	1,050
Visual Basic for Applications	979	936
Vue	732	242
Wolfram	940	973
Wren	358	279,258
XAML	703	24
XCode Config	200	11
XML	605	1,033
XML Schema	1,008	248
Xtend	710	120
YAML	165	47,327
Zig	188	724
Zsh	300	9
gitignore	33	3
ignore	6	2
m4	959	807
nuspec	187	193
sed	82	33

Average complexity for file in each language?

What’s the average complexity per file for each language?

The complexity estimate isn’t really directly comparable between languages. Pulling from the README of scc itself

The complexity estimate is really just a number that is only comparable to files in the same language. It should not be used to compare languages directly without weighting them. The reason for this is that its calculated by looking for branch and loop statements in the code and incrementing a counter for that file.

So comparing languages to each other is not the idea here, although it may be comparable between similar languages such as Java and C for example. Your mileage may vary.

This is more useful when you think about it applying to single files in the same language. So you could answer the question “On average is the file I am working with more or less complex than average?”.

I should mention that I am always looking to improve this calculation and looking for submissions to scc to assist with this goal. Usually it is a case of just adding some keywords to the languages.json file so any programmer of any skill level should be able to assist with this.

skip table to next section

language	complexity
ABAP	11.180740488380376
ASP	11.536947250366211
ASP.NET	2.149275320643484
ATS	0.7621728432717677
AWK	0
ActionScript	22.088579905848178
Ada	13.69141626294931
Agda	0.19536590785719454
Alchemist	0.3423442907696928
Alex	0
Alloy	6.9999997997656465
Android Interface Definition Language	0
Arvo	0
AsciiDoc	0
Assembly	1.5605608227976997
AutoHotKey	423.87785756399626
Autoconf	1.5524294972419739
BASH	7.500000094871363
Basic	1.0001350622574257
Batch	1.4136352496767306
Bazel	6.523681727119303
Bitbake	0.00391388021490391
Bitbucket Pipeline	0
Boo	65.67764583729533
Bosque	236.79837036132812
Brainfuck	27.5516445041791
BuildStream	0
C	220.17236548200242
C Header	0.027589923237434522
C Shell	1.4911166269191476
C#	1.0994400597744005
C++	215.23628287682845
C++ Header	2.2893104921677154
CMake	0.887660006199008
COBOL	0.018726348891789816
CSS	6.317460331175305E-176
CSV	0
Cabal	3.6547924155738194
Cargo Lock	0
Cassius	0
Ceylon	21.664400369259404
Clojure	0.00009155273437716484
ClojureScript	0.5347588658332859
Closure Template	0.503426091716392
CoffeeScript	0.02021490140137264
ColdFusion	6.851776515250336
ColdFusion CFScript	22.287403080299764
Coq	3.3282556015266307
Creole	0
Crystal	1.6065794006138856
Cython	42.87412906489837
D	0
Dart	2.1264450684815657
Device Tree	0
Dhall	0
Docker ignore	0
Dockerfile	6.158891172385556
Document Type Definition	0
Elixir	0.5000612735793482
Elm	5.237952479502043
Emacs Dev Env	1.2701271416728307E-61
Emacs Lisp	0.19531250990197657
Erlang	0.08028322620528387
Expect	0.329944610851471
Extensible Stylesheet Language Transformations	0
F#	0.32300702900710193
F*	9.403954876643223E-38
FIDL	0.12695312593132269
FORTRAN Legacy	0.8337643985574195
FORTRAN Modern	7.5833590276411185
Fish	1.3386242155247368
Flow9	34.5
Forth	2.4664166555765066
Fragment Shader File	0.0003388836600090293
Freemarker Template	10.511094652522283
Futhark	0.8057891242233386
GDScript	10.750000000022537
GLSL	0.6383056697891334
GN	22.400601854287807
Game Maker Language	4.709514207365569
Game Maker Project	0
Gherkin Specification	0.4085178437480328
Go	50.06279203974034
Go Template	2.3866690339840662E-153
Gradle	0
Groovy	3.2506868488244898
HEX	0
HTML	0
Hamlet	0.25053861103978114
Handlebars	1.6943764911351036E-21
Happy	0
Haskell	28.470107150053625
Haxe	66.52873523714804
IDL	7.450580598712868E-9
Idris	17.77642903881352
Intel HEX	0
Isabelle	0.0014658546850726184
JAI	7.749968137734008
JSON	0
JSONL	0
JSX	0.3910405338329044
Jade	0.6881713929215119
Janet	0
Java	297.22908150612085
JavaScript	1.861130583340945
JavaServer Pages	7.24235416213196
Jenkins Buildfile	0
Jinja	0.6118526458846931
Julia	5.779676990326951
Julius	3.7432448068125277
Jupyter	0
Just	1.625490248219907
Korn Shell	11.085027896435056
Kotlin	5.467347841779503
LD Script	6.538079182471746E-26
LESS	0
LEX	0
LOLCODE	5.980839657708373
LaTeX	0
Lean	0.0019872561135834133
License	0
Lisp	4.033602018074421
Lua	44.70686769972825
Lucius	0
Luna	0
MQL Header	82.8036524637758
MQL4	2.9989408299408566
MQL5	32.84198718928553
MSBuild	2.9802322387695312E-8
MUMPS	5.767955578948634E-17
Macromedia eXtensible Markup Language	0
Madlang	8.25
Makefile	3.9272747722381812E-90
Mako	0.007624773579836673
Markdown	0
Meson	0.3975182396400463
Modula3	0.7517121883916386
Module-Definition	0.25000000023283153
Monkey C	9.838715311259486
Mustache	0.00004191328599945435
Nim	0.04812580073302998
Nix	25.500204694250044
OCaml	16.92218069843716
Objective C	65.08967337175548
Objective C++	10.886891531550603
Opalang	1.3724696160763994E-8
Org	28.947825231747235
Oz	6.260657086070324
PHP	2.8314653639690874
PKGBUILD	0
PSL Assertion	0.5009768009185791
Pascal	4
Patch	0
Perl	48.16959255514553
Plain Text	0
Polly	0
Pony	4.91082763671875
Powershell	0.43151378893449877
Processing	9.691001653621564
Prolog	0.5029296875147224
Properties File	0
Protocol Buffers	0.07128906529847256
Puppet	0.16606500436341776
PureScript	1.3008141816356456
Python	11.510142201304832
Q#	5.222080192729404
QCL	13.195626304795667
QML	0.3208023407643109
R	0.40128818821921775
Rakefile	2.75786388297917
Razor	0.5298294073055322
ReStructuredText	0
Report Definition Language	0
Robot Framework	0
Ruby	7.8611656283491795
Ruby HTML	1.3175727506823756
Rust	8.62646485221385
SAS	0.5223999023437882
SKILL	0.4404907226562501
SPDX	0
SQL	0.00001537799835205078
SRecode Template	0.18119949102401853
SVG	1.7686873200833423E-74
Sass	7.002974651049148E-113
Scala	17.522343645163424
Scheme	0.00003147125255509322
Scons	25.56868253610655
Shell	6.409446969197895
Smarty Template	53.06143077491294
Softbridge Basic	7.5
Specman e	0.0639350358484781
Spice Netlist	1.3684555315672042E-48
Standard ML (SML)	24.686901116754818
Stata	1.5115316917094068
Stylus	0.3750006556512421
Swift	0.5793484510104517
Swig	0
SystemVerilog	0.250593163372906
Systemd	0
TCL	96.5072605676113
TOML	0.0048828125000002776
TaskPaper	0
TeX	54.0588040258797
Thrift	0
Twig Template	2.668124511961211
TypeScript	9.191392608918255
TypeScript Typings	6.1642456222327375
Unreal Script	2.7333421227943004
Ur/Web	16.51621568240534
Ur/Web Project	0
V	22.50230618938804
VHDL	18.05495198571289
Vala	147.2761703068509
Varnish Configuration	0
Verilog	5.582400367711671
Verilog Args File	0
Vertex Shader File	0.0010757446297590262
Vim Script	2.4234658314493798
Visual Basic	0.0004882812500167852
Visual Basic for Applications	4.761343429454877
Vue	0.7529517744621779
Wolfram	0.0059204399585724215
Wren	0.08593750013097715
XAML	6.984919309616089E-10
XCode Config	0
XML	0
XML Schema	0
Xtend	2.8245844719990547
YAML	0
Zig	1.0158334437942358
Zsh	1.81697392626756
gitignore	0
ignore	0
m4	0
nuspec	0
sed	22.91158285739948

What’s the average amount of comments used per file in each language?

You could probably rephrase this to asking what developers write the most comments assuming you squint enough.

skip table to next section

language	complexity
ABAP	56.3020026683825
ASP	24.67145299911499
ASP.NET	9.140447860406259E-11
ATS	41.89465025163305
AWK	11.290069486393975
ActionScript	31.3568633027012
Ada	61.269572412982384
Agda	2.4337660860304755
Alchemist	2.232399710231226E-103
Alex	0
Alloy	0.000002207234501959681
Android Interface Definition Language	26.984662160277367
Arvo	0
AsciiDoc	0
Assembly	2.263919769706678E-72
AutoHotKey	15.833985920534857
Autoconf	0.47779749499136687
BASH	34.15625059662068
Basic	1.4219117348874069
Batch	1.0430908205926455
Bazel	71.21859817579139
Bitbake	0.002480246487177871
Bitbucket Pipeline	0.567799577547725
Boo	5.03128187009327
Bosque	0.125244140625
Brainfuck	0
BuildStream	12.84734197699206
C	256.2839210573451
C Header	184.88885430308878
C Shell	5.8409870392823375
C#	30.96563720101839
C++	44.61584829131642
C++ Header	27.578790410119197
CMake	1.7564333047949374
COBOL	0.7503204345703562
CSS	4.998773531463529
CSV	0
Cabal	4.899812531420634
Cargo Lock	0.0703125
Cassius	0.07177734654413487
Ceylon	3.6406326349824667
Clojure	0.0987220821845421
ClojureScript	0.6025725119252456
Closure Template	17.078124673988057
CoffeeScript	1.6345682790069884
ColdFusion	33.745563628665096
ColdFusion CFScript	13.566947396771592
Coq	20.3222774725393
Creole	0
Crystal	6.0308081267588145
Cython	21.0593019957583
D	0
Dart	4.634361584097128
Device Tree	33.64898256434121
Dhall	1.0053101042303751
Docker ignore	8.003553375601768E-11
Dockerfile	4.526245545632278
Document Type Definition	0
Elixir	8.0581139370409
Elm	24.73191350743249
Emacs Dev Env	2.74822998046875
Emacs Lisp	12.168370702306452
Erlang	16.670030919109056
Expect	3.606161126133445
Extensible Stylesheet Language Transformations	0
F#	0.5029605040200058
F*	5.33528354690743E-27
FIDL	343.0418392068642
FORTRAN Legacy	8.121405267242158
FORTRAN Modern	171.32042583820953
Fish	7.979248739519377
Flow9	0.5049991616979244
Forth	0.7578125
Fragment Shader File	0.2373057885016209
Freemarker Template	62.250244379050855
Futhark	0.014113984877253714
GDScript	31.14457228694065
GLSL	0.2182627061047912
GN	17.443267241931284
Game Maker Language	3.9815753922640824
Game Maker Project	0
Gherkin Specification	0.0032959059321794604
Go	6.464829990599041
Go Template	4.460169822267483E-251
Gradle	0.5374194774415457
Groovy	32.32068506016523
HEX	0
HTML	0.16671794164614084
Hamlet	4.203293477836184E-24
Handlebars	0.9389737429747177
Happy	0
Haskell	20.323476462551376
Haxe	9.023509566990532
IDL	1.01534495399968
Idris	0.36279318680267497
Intel HEX	0
Isabelle	4.389802167076498
JAI	2.220446049250313E-16
JSON	0
JSONL	0
JSX	0.9860839844113964
Jade	0.25000000000034117
Janet	9.719207406044006
Java	330.66188089718935
JavaScript	22.102491285372537
JavaServer Pages	4.31250095370342
Jenkins Buildfile	0
Jinja	2.5412145720173454E-50
Julia	12.542627036271085
Julius	0.24612165248208867
Jupyter	0
Just	0.3186038732601446
Korn Shell	40.89005232702741
Kotlin	0.3259347784770708
LD Script	3.7613336386434204
LESS	15.495439701029127
LEX	55.277186392539086
LOLCODE	13.578125958700468
LaTeX	3.316717967334341
Lean	21.194565176965895
License	0
Lisp	88.10676444837796
Lua	76.67247973843406
Lucius	0.3894241626790286
Luna	16.844066019174637
MQL Header	82.22436339969337
MQL4	1.957314499740677
MQL5	27.463183855085845
MSBuild	0.19561428198176206
MUMPS	5.960464477541773E-8
Macromedia eXtensible Markup Language	0
Madlang	6.75
Makefile	1.2287070602578574
Mako	1.3997604187154047E-8
Markdown	0
Meson	4.594536366188615
Modula3	3.4375390004645627
Module-Definition	7.754887182446689
Monkey C	0.02734480644075532
Mustache	0.0000038370490074157715
Nim	0.8432132130061808
Nix	165.09375
OCaml	27.238212826702338
Objective C	32.250000004480256
Objective C++	4.688333711547599
Opalang	3.2498599900436704
Org	2.4032862186444435
Oz	11.531631554476924
PHP	0.37573912739754056
PKGBUILD	0
PSL Assertion	4.470348358154297E-7
Pascal	274.7797153576955
Patch	0
Perl	42.73014043490598
Plain Text	0
Polly	0
Pony	0.2718505859375
Powershell	2.0956492198317282
Processing	11.358358417519032
Prolog	6.93889390390723E-17
Properties File	4.297774864451927
Protocol Buffers	5.013992889700926
Puppet	1.9962931947466012
PureScript	6.608705271035433
Python	15.208443286809963
Q#	0.4281108849922295
QCL	13.880147817629737
QML	16.17036877582475
R	5.355639399818855
Rakefile	0.4253943361101697
Razor	0.2500305203720927
ReStructuredText	0
Report Definition Language	1.8589575837924928E-119
Robot Framework	0
Ruby	8.696056880656087
Ruby HTML	0.031281024218515086
Rust	22.359375028118006
SAS	0.7712382248290134
SKILL	0.002197265625
SPDX	0
SQL	0.4963180149979617
SRecode Template	17.64534428715706
SVG	0.780306812508952
Sass	1.6041624981030795
Scala	2.7290137764062656
Scheme	18.68675828842983
Scons	9.985132321266597
Shell	19.757167057040007
Smarty Template	0.0009841919236350805
Softbridge Basic	4.76177694441164E-25
Specman e	0.1925095270881778
Spice Netlist	5.29710110812646
Standard ML (SML)	0.20708566564292288
Stata	0.04904100534194722
Stylus	4.534405773074049
Swift	1.8627019961192913E-9
Swig	11.786422730001505
SystemVerilog	0.00009708851624323821
Systemd	0
TCL	382.839838598133
TOML	0.37500173695180483
TaskPaper	0
TeX	8.266233975096164
Thrift	50.53134153016524
Twig Template	0
TypeScript	8.250029131770134
TypeScript Typings	37.89904005334354
Unreal Script	46.13322029508541
Ur/Web	0.04756343913582129
Ur/Web Project	6.776263578034403E-21
V	28.75797889154211
VHDL	37.47892257625405
Vala	74.26528331441615
Varnish Configuration	19.45791923156868
Verilog	4.165537942430622
Verilog Args File	0
Vertex Shader File	1.7979557178975683
Vim Script	0
Visual Basic	0.26300267116040704
Visual Basic for Applications	0.3985138943535276
Vue	5.039982162930666E-52
Wolfram	70.01674025323683
Wren	30694.003311276458
XAML	0.5000169009533838
XCode Config	13.653495818959595
XML	3.533205032457776
XML Schema	0
Xtend	19.279739396268607
YAML	1.1074293861154887
Zig	0.507775428428431
Zsh	6.769231127673729
gitignore	1.3347179947709417E-20
ignore	0.0356445312500015
m4	5.4183238737327075
nuspec	3.640625
sed	6.423678000929861

What are the most common filenames?

What filenames are most common across all code-bases ignoring extension and case?

Had you asked me before I started this I would have said, README, main, index, license. Thankfully the results reflect my thoughts pretty well. Although there are a lot of interesting ones in there. I have no idea why so many projects contain a file called 15 or s15.

The makefile being the most common surprised me a little, but then I remembered it is used in many new JavaScript projects. Another interesting thing to note is that it appears jQuery is still king and reports of its death are greatly exaggerated, with it appearing as #4 on the list.

file-name	count
makefile	59,141,098
index	33,962,093
readme	22,964,539
jquery	20,015,171
main	12,308,009
package	10,975,828
license	10,441,647
__init__	10,193,245
strings	8,414,494
android	7,915,225
config	7,391,812
default	5,563,255
build	5,510,598
setup	5,291,751
test	5,282,106
irq	4,914,052
15	4,295,032
country	4,274,451
pom	4,054,543
io	3,642,747
system	3,629,821
common	3,629,698
gpio	3,622,587
core	3,571,098
module	3,549,789
init	3,378,919
dma	3,301,536
bootstrap	3,162,859
application	3,000,210
time	2,928,715
cmakelists	2,907,539
plugin	2,881,206
base	2,805,340
s15	2,733,747
androidmanifest	2,727,041
cache	2,695,345
debug	2,687,902
file	2,629,406
app	2,588,208
version	2,580,288
assemblyinfo	2,485,708
exception	2,471,403
project	2,432,361
util	2,412,138
user	2,343,408
clock	2,283,091
timex	2,280,225
pci	2,231,228
style	2,226,920
styles	2,212,127

Note that due to memory constraints I made this process slightly lossy. Every 100 projects checked I would check the map and if an identified filename had < 10 counts it was dropped from the list. It could come back for the next run and if there was > 10 at this point it would remain. It shouldn’t happen that often but it is possible the counts may be out by some amount if some common name appeared sparsely in the first batch of repositories before becoming common. In short they are not absolute numbers but should be close enough.

I could have used a trie structure to “compress” the space and gotten absolute numbers for this, but I didn’t feel like writing one and just abused the map slightly to save enough memory and achieve my goal. I am however curious enough to try this out at a later date to see how a trie would perform.

How many repositories appear to be missing a license?

This is an interesting one. Which repositories have an explicit license file somewhere? Note that the lack of a license file here does not mean that the project has none, as it might exist within the README or be indicated through SPDX comment tags in-line. it just means that scc could not find an explicit license file using its own criteria which at time of writing means a file ignoring case named “license”, “licence”, “copying”, “copying3”, “unlicense”, “unlicence”, “license-mit”, “licence-mit” or “copyright”.

Sadly it appears that the vast majority of repositories are missing a license. I would argue that all software should have a license for a variety of reasons but here is someone else’s take on that.

has license	count
no	6,502,753
yes	2,597,330

How many projects use multiple .gitignore files?

Some may not know this but it is possible to have multiple .gitignore files in a git project. Given that fact how many projects use multiple .gitignore files? While we are looking how many have none?

What I did find that was interesting was one project that has 25,794 .gitignore files in its repository. The next highest was 2,547. I have no idea what is going on there. I had a brief look at it and it looks like they are used to allow checking in of the directories but I cannot confirm this.

Bringing this back to something sensible here is a plot of the data up to 20 .gitignore files and close to 99% of the total result.

Something you would expect would be that the majority of projects would have either 0 or 1 .gitignore files. This is confirmed by the results with a massive drop-off of 10x for projects with 2 .gitignores. What was surprising to me was how many projects have more than a single .gitignore file. The long tail is especially long in this case.

I was also curious as to why some projects had thousands of .gitignore files. One of the main offenders appears to be forks of https://github.com/PhantomX/slackbuilds which all have ~2,547 .gitignore files. However the other repositories with 1000+ ignore files are listed below.

skip table to next section

.gitignore count	project count
0	3,628,829
1	4,576,435
2	387,748
3	136,641
4	79,808
5	48,336
6	33,686
7	33,408
8	22,571
9	16,453
10	11,198
11	10,070
12	8,194
13	7,701
14	5,040
15	4,320
16	5,905
17	4,156
18	4,542
19	3,828
20	2,706
21	2,449
22	1,975
23	2,255
24	2,060
25	1,768
26	2,886
27	2,648
28	2,690
29	1,949
30	1,677
31	3,348
32	1,176
33	794
34	1,153
35	845
36	488
37	627
38	533
39	502
40	398
41	370
42	355
43	1,002
44	265
45	262
46	295
47	178
48	384
49	270
50	189
51	435
52	202
53	196
54	325
55	253
56	320
57	126
58	329
59	286
60	292
61	152
62	237
63	163
64	149
65	187
66	164
67	92
68	80
69	138
70	102
71	68
72	62
73	178
74	294
75	89
76	118
77	110
78	319
79	843
80	290
81	162
82	127
83	147
84	170
85	275
86	1,290
87	614
88	4,014
89	2,275
90	775
91	3,630
92	362
93	147
94	110
95	71
96	75
97	62
98	228
99	71
100	174
101	545
102	304
103	212
104	284
105	516
106	236
107	39
108	69
109	131
110	82
111	102
112	465
113	621
114	47
115	59
116	43
117	40
118	43
119	443
120	72
121	42
122	33
123	392
124	66
125	46
126	381
127	19
128	99
129	906
130	52
131	19
132	11
133	99
134	10
135	15
136	6
137	22
138	44
139	33
140	24
141	33
142	39
143	48
144	80
145	20
146	28
147	19
148	17
149	11
150	20
151	57
152	35
153	24
154	31
155	35
156	55
157	89
158	57
159	88
160	18
161	47
162	56
163	36
164	63
165	99
166	44
167	64
168	86
169	70
170	111
171	106
172	25
173	39
174	14
175	25
176	53
177	20
178	56
179	11
180	7
181	40
182	32
183	17
184	68
185	38
186	16
187	3
188	4
189	2
190	12
191	18
192	37
193	9
194	10
195	11
196	18
197	45
198	27
199	11
200	39
201	23
202	37
203	22
204	21
205	7
206	40
207	7
208	8
209	16
210	29
211	20
212	21
213	7
214	4
215	12
217	21
218	13
220	12
221	2
222	15
223	4
224	12
225	9
226	1
227	8
228	3
229	6
230	8
231	31
232	26
233	6
234	17
235	6
236	23
237	1
238	11
239	2
240	10
241	7
242	11
243	1
244	14
245	21
246	3
247	12
248	1
249	6
250	10
251	5
252	18
253	7
254	17
255	4
256	16
257	8
258	24
259	17
260	4
261	1
262	3
263	12
264	3
265	8
267	2
268	1
269	3
271	4
272	1
273	1
274	1
275	3
276	6
279	5
280	1
281	1
284	4
285	1
286	1
288	2
289	1
290	5
291	4
293	7
294	4
295	1
296	1
297	1
299	70
300	2
301	4
302	1
303	7
305	1
306	2
307	2
309	1
310	7
311	1
313	14
316	1
320	1
321	6
322	2
323	3
324	4
327	4
328	2
329	1
330	13
331	5
332	11
333	3
334	1
335	1
336	11
337	1
338	20
339	11
340	2
341	6
342	10
343	37
344	25
345	9
346	32
347	4
348	9
349	7
350	12
351	2
352	5
354	7
358	32
359	7
360	6
361	1
362	21
363	14
364	51
365	17
367	18
368	9
370	7
371	6
372	15
373	1
374	38
375	113
376	57
377	37
378	23
379	87
380	65
382	1
386	2
388	1
391	5
392	1
394	1
397	3
401	1
403	1
408	1
409	2
410	5
411	1
413	4
415	1
418	1
420	1
427	3
428	2
430	2
433	314
437	1
450	2
453	1
468	1
469	1
483	5
484	1
486	1
488	2
489	9
490	4
492	2
493	106
494	3
495	1
496	2
498	1
512	1
539	1
553	1
560	2
570	2
600	1
602	3
643	1
646	2
657	1
663	1
670	1
672	2
729	5
732	1
739	1
744	1
759	1
778	1
819	1
859	1
956	1
959	2
964	2
965	1
973	1
1,133	1
1,186	1
1,267	2
1,523	1
2,535	1
2,536	1
2,537	2
2,539	1
2,540	1
2,541	5
2,542	1
2,545	1
2,547	1
25,794	1

Which language developers have the biggest potty mouth?

Working this out is not an exact science. It falls into the NLP class of problems really. Picking up cursing/swearing or offensive terms using filenames from a defined list is never going to be effective. If you do a simple string contains test you pick up all sorts or normal files such as assemble.sh and such. So to produce the following I pulled a list of curse words, then checked if any files in each project start with one of those values followed by a period. This would mean a file named gangbang.java would be picked up while assemble.sh would not. However this is going to miss all sorts of cases such as pu55syg4rgle.java and other such crude names.

The list I used contained some leet speak such as b00bs and b1tch to try and catch some of the most interesting cases. The full list is here.

While not accurate at all as mentioned it is incredibly fun to see what this produces. So lets start with a list of which languages have the most curse words. However we should probably weight this against how much code exists as well. So here are the top ones.

language	filename curse count	percent of files
C Header	7,660	0.00126394567906%
Java	7,023	0.00258792635479%
C	6,897	0.00120706524533%
PHP	5,713	0.00283428484703%
JavaScript	4,306	0.00140692338568%
HTML	3,560	0.00177646776919%
Ruby	3,121	0.00223136542655%
JSON	1,598	0.00293688627715%
C++	1,543	0.00135977378652%
Dart	1,533	0.19129310646%
Rust	1,504	0.038465935524%
Go Template	1,500	0.0792233157387%
SVG	1,234	0.00771043360379%
XML	1,212	0.000875741051608%
Python	1,092	0.00119138129893%
JavaServer Pages	1,037	0.0215440542669%

Interesting! My first thought was “those naughty C developers!” but as it turns out while they have a high count they write so much code it probably isn’t that big a deal. However pretty clearly Dart developers have an axe to grind! If you know someone coding in Dart you may want to go offer them a hug.

I also want to know what are the most commonly used curse words. Lets see how dirty a mind we have collectively. A few of the top ones I could see being legitimate names (if you squint), but the majority would certainly produce few comments in a PR and a raised eyebrow.

word	count
ass	11,358
knob	10,368
balls	8,001
xxx	7,205
sex	5,021
nob	3,385
pawn	2,919
hell	2,819
crap	1,112
anal	950
snatch	885
fuck	572
poop	510
cox	476
shit	383
lust	367
butt	265
bum	151
bugger	132
pron	121
cum	118
cok	112
damn	105

Note that some of the more offensive words in the list did have matching filenames which I find rather shocking considering what they were. Thankfully they were not very common and didn’t make my list above which was limited to those which had counts over 100. I am hoping that those files only exist for testing allow/deny lists and such.

Longest files by lines per language

As you would probably expect Plain Text, SQL, XML, JSON and CSV take the top positions of this one, seeing as they usually contain meta-data, database dumps and the like.

Limited to 40 because at some point there is only a hello world example or such available and the result is not very interesting. It is not surprising to see that someone has checked in sqlite3.c somewhere but I would be a little worried about that 3,064,594 line Python file and that 1,997,637 line TypeScript monster.

NB Some of the links below MAY not translate 100% due to throwing away some information when I created the files. Most should work, but a few you may need to mangle the URL to resolve.

skip table to next section

language	filename	lines
Plain Text	1366100696temp.txt	347,671,811
PHP	phpfox_error_log_04_04_12_3d4b11f6ee2a89fd5ace87c910cee04b.php	121,930,973
HTML	yo.html	54,596,752
LEX	l	39,743,785
XML	dblp.xml	39,445,222
Autoconf	21-t2.in	33,526,784
CSV	ontology.csv	31,946,031
Prolog	top500_full.p	22,428,770
JavaScript	mirus.js	22,023,354
JSON	douglasCountyVoterRegistration.json	21,104,668
Game Maker Language	lg.gml	13,302,632
C Header	trk6data.h	13,025,371
Objective C++	review-1.mm	12,788,052
SQL	newdump.sql	11,595,909
Patch	clook_iosched-team01.patch	10,982,879
YAML	data.yml	10,764,489
SVG	large-file.svg	10,485,763
Sass	large_empty.scss	10,000,000
Assembly	J.s	8,388,608
LaTeX	tex	8,316,556
C++ Header	primpoly_impl.hh	8,129,599
Lisp	simN.lsp	7,233,972
Perl	aimlCore3.pl	6,539,759
SAS	output.sas	5,874,153
C	CathDomainDescriptionFile.v3.5.c	5,440,052
Lua	giant.lua	5,055,019
R	disambisearches.R	4,985,492
MUMPS	ref.mps	4,709,289
HEX	combine.hex	4,194,304
Python	mappings.py	3,064,594
Scheme	atomspace.scm	3,027,366
C++	Int.cpp	2,900,609
Properties File	nuomi_active_user_ids.properties	2,747,671
Alex	Dalek.X	2,459,209
TCL	TCL	2,362,970
Ruby	smj_12_2004.rb	2,329,560
Wolfram	hmm.nb	2,177,422
Brainfuck	BF	2,097,158
TypeScript	all_6.ts	1,997,637
Module-Definition	matrix.def	1,948,817
LESS	less	1,930,356
Objective C	faster.m	1,913,966
Org	default.org	1,875,096
Jupyter	ReHDDM - AllGo sxFits-Copy0.ipynb	1,780,197
Specman e	twitter.e	1,768,135
F*	Pan_troglodytes_monomers.fst	1,739,878
Systemd	video_clean_lower_tokenized.target	1,685,570
V	ImageMazeChannelValueROM.v	1,440,068
Markdown	eukaryota.md	1,432,161
TeX	japanischtest.tex	1,337,456
Forth	europarl.tok.fr	1,288,074
Shell	add_commitids_to_src.sh	1,274,873
SKILL	hijacked.il	1,187,701
CSS	7f116c3.css	1,170,216
C#	Form1.cs	1,140,480
gitignore	.gitignore	1,055,167
Boo	3.out.tex	1,032,145
Java	Monster.java	1,000,019
ActionScript	as	1,000,000
MSBuild	train.props	989,860
D	D	883,308
Coq	CompiledDFAs.v	873,354
Clojure	raw-data.clj	694,202
Swig	3DEditor.i	645,117
Happy	y	624,673
GLSL	capsid.vert	593,618
Verilog	pipeline.vg	578,418
Standard ML (SML)	Ambit3-HRVbutNoHR.sml	576,071
SystemVerilog	bitcoinminer.v	561,974
Visual Basic	linqStoreProcs.designer.vb	561,067
Go	info.go	559,236
Expect	Argonne_hourly_dewpoint.exp	552,269
Erlang	sdh_analogue_data.erl	473,924
Makefile	Makefile	462,433
QML	2005.qml	459,113
SPDX	linux-coreos.spdx	444,743
VHDL	cpuTest.vhd	442,043
ASP.NET	AllProducts.aspx	438,423
XML Schema	AdvanceShipNotices.xsd	436,055
Elixir	gene.train.with.rare.ex	399,995
Macromedia eXtensible Markup Language	StaticFlex4PerformanceTest20000.mxml	399,821
Ada	bmm_top.adb	390,275
TypeScript Typings	dojox.d.ts	384,171
Pascal	FHIR.R4.Resources.pas	363,291
COBOL	cpy	358,745
Basic	excel-vba-streams-#1.bas	333,707
Visual Basic for Applications	Dispatcher.cls	332,266
Puppet	main_110.pp	314,217
FORTRAN Legacy	f	313,599
OCaml	Pent.ML	312,749
FORTRAN Modern	slatec.f90	298,677
CoffeeScript	dictionary.coffee	271,378
Nix	hackage-packages.nix	259,940
Intel HEX	epdc_ED060SCE.fw.ihex	253,836
Scala	models_camaro.sc	253,559
Julia	IJulia 0.jl	221,058
SRecode Template	espell.srt	216,243
sed	CSP-2004fe.SED	214,290
ReStructuredText	S40HO033.rst	211,403
Bosque	world_dem_5arcmin_geo.bsq	199,238
Emacs Lisp	ubermacros.el	195,861
F#	Ag_O1X5.5_O2X0.55.eam.fs	180,008
GDScript	72906.gd	178,628
Gherkin Specification	feature	175,229
Haskell	Excel.hs	173,039
Dart	surnames_list.dart	153,144
Bazel	matplotlib_1.3.1-1_amd64-20140427-1441.build	149,234
Haxe	elf-x86id.hx	145,800
IDL	all-idls.idl	129,435
LD Script	kernel_partitions.lds	127,187
Monkey C	LFO_BT1-point.mc	120,881
Modula3	tpch22.m3	120,185
Batch	EZhunter.cmd	119,341
Rust	data.rs	114,408
Ur/Web	dict.ur-en.ur	113,911
Unreal Script	orfs.derep_id97.uc	110,737
Groovy	groovy	100,297
Smarty Template	assign.100000.tpl	100,002
Bitbake	bb	100,000
BASH	palmer-master-thesis.bash	96,911
PSL Assertion	test_uno.psl	96,253
ASP	sat_gbie_01.asp	95,144
Protocol Buffers	select1.proto	89,796
Report Definition Language	ACG.rdl	84,666
Powershell	PresentationFramework.ps1	83,861
Jinja	jinja2	76,040
AWK	words-large.awk	69,964
LOLCODE	lol	67,520
Wren	reuse_constants.wren	65,550
JSX	AEscript.jsx	65,108
Rakefile	seed.rake	63,000
Stata	.31113.do	60,343
Vim Script	ddk.vim	60,282
Swift	Google.Protobuf.UnittestEnormousDescriptor.proto.swift	60,236
Korn Shell	attachment-0002.ksh	58,298
AsciiDoc	index.adoc	52,627
Freemarker Template	designed.eml.ftl	52,160
Cython	CALC.pex.netlist.CALC.pxi	50,283
m4	ax.m4	47,828
Extensible Stylesheet Language Transformations	green_ccd.xslt	37,247
License	copyright	37,205
JavaServer Pages	1MB.jsp	36,007
Document Type Definition	bookmap.dtd	32,815
Fish	Godsay.fish	31,112
ClojureScript	core.cljs	31,013
Robot Framework	robot	30,460
Processing	data.pde	30,390
Ruby HTML	big_table.rhtml	29,306
ColdFusion	spreadsheet2009Q1.cfm	27,974
CMake	ListOfVistARoutines.cmake	27,550
ATS	test06.dats	24,350
Nim	windows.nim	23,949
Vue	Ogre.vue	22,916
Razor	validationerror.cshtml	22,832
Spice Netlist	input6.ckt	22,454
Isabelle	WooLam_cert_auto.thy	22,312
XAML	SymbolDrawings.xaml	20,764
Opalang	p4000_g+5.0_m0.0_t00_st_z+0.00_a+0.00_c+0.00_n+0.00_o+0.00_r+0.00_s+0.00.opa	20,168
TOML	too_large.toml	20,000
Madlang	evgg.mad	19,416
Stylus	test.styl	19,127
Go Template	html-template.tmpl	19,016
AutoHotKey	glext.ahk	18,036
ColdFusion CFScript	IntakeHCPCIO.cfc	17,606
Zsh	_oc.zsh	17,307
Twig Template	show.html.twig	16,320
ABAP	ZRIM01F01.abap	16,029
Elm	57chevy.elm	14,968
Kotlin	_Arrays.kt	14,396
Varnish Configuration	40_generic_attacks.vcl	13,367
Mustache	huge.mustache	13,313
Alloy	output.als	12,168
Device Tree	tegra132-flounder-emc.dtsi	11,893
MQL4	PhD Appsolute System.mq4	11,280
Jade	fugue.jade	10,711
Q#	in_navegador.qs	10,025
JSONL	train.jsonl	10,000
Flow9	graph2.flow	9,902
Vala	mwp.vala	8,765
Handlebars	theme.scss.hbs	8,259
Crystal	CR	8,084
C Shell	plna.csh	8,000
Hamlet	hamlet	7,882
BuildStream	biometrics.bst	7,746
Mako	verificaciones.mako	7,306
Agda	Pifextra.agda	6,483
Thrift	concourse.thrift	6,471
Fragment Shader File	ms812_bseqoslabel_l.fsh	6,269
Cargo Lock	Cargo.lock	6,202
Xtend	UMLSlicerAspect.xtend	5,936
Arvo	test-extra-large.avsc	5,378
Scons	SConstruct	5,272
Closure Template	buckconfig.soy	5,189
GN	BUILD.gn	4,653
Softbridge Basic	owptext.sbl	4,646
PKGBUILD	PKGBUILD	4,636
Oz	StaticAnalysis.oz	4,500
Lucius	bootstrap.lucius	3,992
Ceylon	RedHatTransformer.ceylon	3,907
Creole	MariaDB_Manager_Monitors.creole	3,855
Luna	Base.luna	3,731
Gradle	dependencies.gradle	3,612
MQL Header	IncGUI.mqh	3,544
Cabal	smartword.cabal	3,452
Emacs Dev Env	ede	3,400
Meson	meson.build	3,264
nuspec	Npm.js.nuspec	2,823
Game Maker Project	LudumDare.yyp	2,679
Julius	default-layout.julius	2,454
Idris	ring_reduce.idr	2,434
Alchemist	out.lmf-dos.crn	2,388
MQL5	DTS1-Build_814.1_B-test~.mq5	2,210
Android Interface Definition Language	ITelephony.aidl	2,005
Vertex Shader File	sdk_macros.vsh	1,922
Lean	interactive.lean	1,664
Jenkins Buildfile	Jenkinsfile	1,559
FIDL	amb.in.fidl	1,502
Pony	scenery.pony	1,497
PureScript	prelude.purs	1,225
TaskPaper	task-3275.taskpaper	1,196
Dockerfile	Dockerfile	1,187
Janet	Janet	1,158
Futhark	math.fut	990
Zig	main.zig	903
XCode Config	Project-Shared.xcconfig	522
JAI	LCregistryFile.jai	489
QCL	bwt.qcl	447
Ur/Web Project	reader.urp	346
Cassius	default-layout.cassius	313
Docker ignore	.dockerignore	311
Dhall	largeExpressionA.dhall	254
ignore	.ignore	192
Bitbucket Pipeline	bitbucket-pipelines.yml	181
Just	Justfile	95
Verilog Args File	or1200.irunargs	60
Polly	polly	26

Whats the most complex file in each language?

Once again these values are not directly comparable to each other, but it is interesting to see what is considered the most complex in each language.

Some of these files are absolute monsters. For example consider the most complex C++ file I found COLLADASaxFWLColladaParserAutoGen15PrivateValidation.cpp which is 28.3 MB of compiler hell (and thankfully appears to be generated).

NB Some of the links below MAY not translate 100% due to throwing away some information when I created the files. Most should work, but a few you may need to mangle the URL to resolve.

skip table to next section

language	filename	complexity
C++	COLLADASaxFWLColladaParserAutoGen15PrivateValidation.cpp	682,001
JavaScript	blocks.js	582,070
C Header	bigmofoheader.h	465,589
C	fmFormula.c	445,545
Objective C	faster.m	409,792
SQL	dump20120515.sql	181,146
ASP.NET	results-i386.master	164,528
Java	ConcourseService.java	139,020
TCL	68030_TK.tcl	136,578
C++ Header	TPG_hardcoded.hh	129,465
TypeScript Typings	all.d.ts	127,785
SVG	Class Diagram2.svg	105,353
Lua	luaFile1000kLines.lua	102,960
PHP	fopen.php	100,000
Org	2015-02-25_idfreeze-2.org	63,326
Ruby	all_search_helpers.rb	60,375
Scheme	test.ss	50,000
Stata	.31113.do	48,600
Elixir	pmid.sgd.crawl.ex	46,479
Brainfuck	Poll.bf	41,399
Perl	r1d7.pl	41,128
Go	segment_words_prod.go	34,715
Python	lrparsing-sqlite.py	34,700
Module-Definition	wordnet3_0.def	32,008
Clojure	raw-data.clj	29,950
C#	Matrix.Product.Generated.cs	29,675
D	parser.d	27,249
FORTRAN Modern	euitm_routines_407c.f90	27,161
Puppet	sqlite3.c.pp	25,753
SystemVerilog	6s131.sv	24,300
Autoconf	Makefile.in	23,183
Specman e	hansards.e	20,893
Smarty Template	test-include-09.tpl	20,000
TypeScript	JSONiqParser.ts	18,162
V	altera_mf.v	13,584
F*	slayer-3.fst	13,428
TeX	definitions.tex	13,342
Swift	Google.Protobuf.UnittestEnormousDescriptor.proto.swift	13,017
Assembly	all-opcodes.s	12,800
Bazel	firebird2.5_2.5.2.26540.ds4-10_amd64-20140427-2159.build	12,149
FORTRAN Legacy	lm67.F	11,837
R	Rallfun-v36.R	11,287
ActionScript	AccessorSpray.as	10,804
Haskell	Tags.hs	10,444
Prolog	books_save.p	10,243
Dart	DartParser.dart	9,606
VHDL	unisim_VITAL.vhd	9,590
Batch	test.bat	9,424
Boo	compman.tex	9,280
Coq	NangateOpenCellLibrary.v	8,988
Shell	i3_completion.sh	8,669
Kotlin	1.kt	7,388
JSX	typescript-parser.jsx	7,123
Makefile	Makefile	6,642
Emacs Lisp	bible.el	6,345
Objective C++	set.mm	6,285
OCaml	sparcrec.ml	6,285
Expect	condloadstore.stdout.exp	6,144
SAS	import_REDCap.sas	5,783
Julia	pilot-2013-05-14.jl	5,599
Cython	types.pyx	5,278
Modula3	tpch22.m3	5,182
Haxe	T1231.hx	5,110
Visual Basic for Applications	Coverage.cls	5,029
Lisp	simN.lsp	4,994
Scala	SpeedTest1MB.sc	4,908
Groovy	ZulTagLib.groovy	4,714
Powershell	PresentationFramework.ps1	4,108
Ada	bhps-print_full_version.adb	3,961
JavaServer Pages	sink_jq.jsp	3,850
GN	patch-third_partyffmpegffmpeg_generated.gni	3,742
Basic	MSA_version116_4q.bas	3,502
Pascal	Python_StdCtrls.pas	3,399
Standard ML (SML)	arm.sml	3,375
Erlang	lipsum.hrl	3,228
ASP	mylib.asp	3,149
CSS	three-viewer.css	3,071
Unreal Script	ScriptedPawn.uc	2,909
CoffeeScript	game.coffee	2,772
AutoHotKey	fishlog5.93.ahk	2,764
MQL4	PhD Appsolute System.mq4	2,738
Processing	Final.pde	2,635
Isabelle	StdInst.thy	2,401
Razor	Checklist.cshtml	2,341
Sass	_multi-color-css-stackicons-social.scss	2,325
Vala	valaccodebasemodule.vala	2,100
MSBuild	all.props	2,008
Rust	ffi.rs	1,928
QML	Dots.qml	1,875
F#	test.fsx	1,826
Vim Script	netrw.vim	1,790
Korn Shell	attachment.ksh	1,773
Vue	vue	1,738
sed	SED	1,699
GLSL	comp	1,699
Nix	auth.nix	1,615
Mustache	template.mustache	1,561
Bitbake	my-2010.bb	1,549
Ur/Web	votes.ur	1,515
BASH	pgxc_ctl.bash	1,426
MQL Header	hanoverfunctions.mqh	1,393
Visual Basic	LGMDdataDataSet.Designer.vb	1,369
Q#	flfacturac.qs	1,359
C Shell	regtest_hwrf.csh	1,214
MQL5	DTS1-Build_814.1_B-test~.mq5	1,186
Xtend	Parser.xtend	1,116
Nim	disas.nim	1,098
CMake	MacroOutOfSourceBuild.cmake	1,069
Protocol Buffers	configure.proto	997
SKILL	switch.il	997
COBOL	geekcode.cob	989
Game Maker Language	hydroEx_River.gml	982
Gherkin Specification	upload_remixed_program_again_complex.feature	959
Alloy	battleformulas.als	948
Bosque	recover.bsq	924
ColdFusion	jquery.js.cfm	920
Stylus	buttron.styl	866
ColdFusion CFScript	apiUtility.cfc	855
Verilog	exec_matrix.vh	793
Freemarker Template	DefaultScreenMacros.html.ftl	771
Crystal	lexer.cr	753
Forth	e4	690
Monkey C	mc	672
Rakefile	import.rake	652
Zsh	zshrc	649
Ruby HTML	ext_report.rhtml	633
Handlebars	templates.handlebars	557
SRecode Template	Al3SEbeK61s.srt	535
Scons	SConstruct	522
Agda	Square.agda	491
Ceylon	runtime.ceylon	467
Julius	default-layout.julius	436
Wolfram	qmSolidsPs8dContourPlot.nb	417
Cabal	parconc-examples.cabal	406
Fragment Shader File	flappybird.fsh	349
ATS	ats_staexp2_util1.dats	311
Jinja	php.ini.j2	307
Opalang	unicode.opa	306
Twig Template	product_form.twig	296
ClojureScript	core.cljs	271
Hamlet	hamlet	270
Oz	StaticAnalysis.oz	267
Elm	Indexer.elm	267
Meson	meson.build	248
ABAP	ZRFFORI99.abap	244
Dockerfile	Dockerfile	243
Wren	repl.wren	242
Fish	fisher.fish	217
Emacs Dev Env	ede	211
GDScript	tiled_map.gd	195
IDL	bgfx.idl	187
Jade	docs.jade	181
PureScript	List.purs	180
XAML	Midnight.xaml	179
Flow9	TypeMapper.js.flow	173
Idris	Utils.idr	166
PSL Assertion	pre_dec.psl	162
Lean	kernel.lean	161
MUMPS	link.mps	161
Vertex Shader File	base.vsh	152
Go Template	code-generator.tmpl	148
Mako	pokemon.mako	137
Closure Template	template.soy	121
Zig	main.zig	115
TOML	telex_o.toml	100
Softbridge Basic	asm.sbl	98
QCL	bwt.qcl	96
Futhark	math.fut	86
Pony	jstypes.pony	70
LOLCODE	LOLTracer.lol	61
Alchemist	alchemist.crn	55
Madlang	Copying.MAD	44
LD Script	plugin.lds	39
Device Tree	dts	22
FIDL	GlobalCapabilitiesDirectory.fidl	19
JAI	LICENSE.jai	18
Just	Justfile	7
Android Interface Definition Language	aidl	3
Ur/Web Project	jointSpace.urp	2
Spice Netlist	GRI30.CKT	2

Whats the most complex file weighted against lines?

This sounds good in practice, but in reality… anything minified or with no newlines skews the results making this one effectively pointless. As such I have not included this calculation. I have however created an issue inside scc to support detection of minified code so it can be removed from the calculation results https://github.com/boyter/scc/issues/91

It’s probably possible to infer this using just the data at hand, but id like to make it a more robust check that anyone using scc can benefit from.

Whats the most commented file in each language? I have no idea what sort of information you can get out of this that might be useful but it is interesting to have a look.

NB Some of the links below MAY not translate 100% due to throwing away some information when I created the files. Most should work, but a few you may need to mangle the URL to resolve.

skip table to next section

language	filename	comment lines
Prolog	ts-with-score-multiplier.p	5,603,870
C	testgen.c	1,705,508
Python	Untitled0.py	1,663,466
JavaScript	100MB.js	1,165,656
SVG	p4-s3_I369600.svg	1,107,955
SQL	test.sql	858,993
C Header	head.h	686,587
C++	ResidueTopology.cc	663,024
Autoconf	square_detector_local.in	625,464
TypeScript	reallyLargeFile.ts	583,708
LEX	polaris-xp900.l	457,288
XML	Test1-CDL-soapui-project.xml	411,321
HTML	todos_centros.html	366,776
Pascal	FHIR.R4.Resources.pas	363,289
SystemVerilog	mkToplevelBT64.v	338,042
PHP	lt.php	295,054
TypeScript Typings	dojox.d.ts	291,002
Verilog	CVP14_synth.vg	264,649
Lua	objects.lua	205,006
V	TestDataset01-functional.v	201,973
Java	FinalPackage.java	198,035
C++ Header	test_cliprdr_channel_xfreerdp_full_authorisation.hpp	196,958
Shell	add_commitids_to_src.sh	179,223
C#	ItemId.cs	171,944
FORTRAN Modern	slatec.f90	169,817
Assembly	HeavyWeather.asm	169,645
Module-Definition	top_level.final.def	139,150
FORTRAN Legacy	dlapack.f	110,640
VHDL	cpuTest.vhd	107,882
Groovy	groovy	98,985
IDL	all-idls.idl	91,771
Wolfram	K2KL.nb	90,224
Go	frequencies.go	89,661
Scheme	s7test.scm	88,907
D	coral.jar.d	80,674
Coq	cycloneiv_hssi_atoms.v	74,936
Specman e	sysobjs.e	65,146
Puppet	sqlite3.c.pp	63,656
Wren	many_globals.wren	61,388
Boo	sun95.tex	57,018
Ruby	bigfile.rb	50,000
Objective C	job_sub011.m	44,788
CSS	screener.css	43,785
Swig	CIDE.I	37,235
Fish	Godsay.fish	31,103
Sass	sm30_kernels.sass	30,306
CoffeeScript	tmp.coffee	29,088
Erlang	nci_ft_ricm_dul_SUITE.erl	28,306
Lisp	km_2-5-33.lisp	27,579
YAML	ciudades.yml	27,168
R	PhyloSimSource.R	26,023
Scala	GeneratedRedeclTests.scala	24,647
Emacs Lisp	pjb-java.el	24,375
Haskell	Dipole80.hs	24,245
ATS	test06.dats	24,179
m4	ax.m4	22,675
ActionScript	__2E_str95.as	21,173
Objective C++	edges-new.mm	20,789
Visual Basic	clsProjections.vb	20,641
TCL	68030_TK.tcl	20,616
Nix	nix	19,605
Perl	LF_aligner_3.12_with_modules.pl	18,013
Ada	amf-internals-tables-uml_metamodel-objects.adb	14,535
Batch	MAS_0.6_en.cmd	14,402
OCaml	code_new.ml	13,648
LaTeX	pm3dcolors.tex	13,092
Properties File	messages_ar_SA.properties	13,074
MSBuild	ncrypto.csproj	11,302
ASP.NET	GallerySettings.ascx	10,969
Powershell	mail_imap.ps1	10,798
Standard ML (SML)	TCP1_hostLTSScript.sml	10,790
Dart	html_dart2js.dart	10,547
AutoHotKey	studio.ahk	10,391
Expect	Navigator.exp	10,063
Julia	PETScRealSingle.jl	9,417
Makefile	Makefile	9,204
Forth	europarl.lowercased.fr	9,107
ColdFusion	js.cfm	8,786
TeX	hyperref.sty	8,591
Opalang	i18n_language.opa	7,860
LESS	_variables.less	7,394
Swift	CodeSystems.swift	6,847
Bazel	gcc-mingw-w64_12_amd64-20140427-2100.build	6,429
Kotlin	_Arrays.kt	5,887
SAS	202_002_Stream_DQ_DRVT.sas	5,597
Haxe	CachedRowSetImpl.hx	5,438
Rust	lrgrammar.rs	5,150
Monkey C	mc	5,044
Cython	pcl_common_172.pxd	5,030
Nim	disas.nim	4,547
Game Maker Language	gm_spineapi.gml	4,345
ABAP	ZACO19U_SHOP_NEW_1.abap	4,244
XAML	Raumplan.xaml	4,193
Razor	Privacy.cshtml	4,092
Varnish Configuration	46_slr_et_rfi_attacks.vcl	3,924
Basic	MSA_version116_4q.bas	3,892
Isabelle	Pick.thy	3,690
Protocol Buffers	metrics_constants.proto	3,682
BASH	bashrc	3,606
Clojure	all-playlists-output.clj	3,440
F#	GenericMatrixDoc.fs	3,383
Thrift	NoteStore.thrift	3,377
COBOL	db2ApiDf.cbl	3,319
JavaServer Pages	sink_jq.jsp	3,204
Modula3	gdb.i3	3,124
Visual Basic for Applications	HL7xmlBuilder.cls	2,987
Oz	timing.oz	2,946
Closure Template	buckconfig.soy	2,915
Agda	Pifextra.agda	2,892
Stata	R2_2cleaningprocess.do	2,660
ColdFusion CFScript	Intake.cfc	2,578
Luna	Base.luna	2,542
Unreal Script	UIRoot.uc	2,449
CMake	cmake	2,425
Org	lens-wsn.org	2,417
Flow9	index.js.flow	2,361
MQL Header	IncGUI.mqh	2,352
JSX	ContactSheetII.jsx	2,243
MQL4	PhD Appsolute System.mq4	2,061
Ruby HTML	FinalOral-Old.Rhtml	2,061
GDScript	group.gd	2,023
Processing	testcode.pde	2,014
PSL Assertion	2016-08-16.psl	2,011
ASP	c_system_plugin.asp	1,878
AWK	dic-generator.awk	1,732
Jinja	php.ini.j2	1,668
Zsh	.zshrc	1,588
Q#	in_navegador.qs	1,568
sed	Makefile.sed	1,554
Stylus	popup.styl	1,550
Bitbake	Doxyfile.bb	1,533
Rakefile	samples.rake	1,509
Gherkin Specification	WorkflowExecution.feature	1,421
Crystal	string.cr	1,412
Android Interface Definition Language	ITelephony.aidl	1,410
Xtend	Properties.xtend	1,363
SKILL	DT_destub.il	1,181
Madlang	.config.mad	1,137
Spice Netlist	APEXLINEAR.ckt	1,114
QML	MainFULL.qml	1,078
GLSL	subPlanetNoise.frag	1,051
Ur/Web	initial.ur	1,018
Alloy	TransactionFeatureFinal.als	1,012
Vala	puzzle-piece.vala	968
Smarty Template	Ensau.tpl	965
Mako	jobs.mako	950
TOML	traefik.toml	938
gitignore	.gitignore	880
Elixir	macros.ex	832
GN	rules.gni	827
Korn Shell	lx_distro_install.ksh	807
LD Script	vmlinux.lds	727
Scons	SConstruct	716
Handlebars	Consent-Form.handlebars	714
Device Tree	ddr4-common.dtsi	695
FIDL	amb.in.fidl	686
Julius	glMatrix.julius	686
C Shell	setup_grid.csh	645
Lean	perm.lean	642
Idris	Overview.idr	637
PureScript	Array.purs	631
Freemarker Template	result_softwares.ftl	573
ClojureScript	lt-cljs-tutorial.cljs	518
Fragment Shader File	bulb.fsh	464
Elm	Attributes.elm	434
Jade	index.jade	432
Vue	form.vue	418
Gradle	build.gradle	416
Lucius	bootstrap.lucius	404
Go Template	fast-path.go.tmpl	400
Meson	meson.build	306
F*	Crypto.Symmetric.Poly1305.Bignum.Lemmas.Part1.fst	289
Ceylon	IdeaCeylonParser.ceylon	286
MQL5	ZigzagPattern_oldest.mq5	282
XCode Config	Project-Shared.xcconfig	265
Futhark	blackscholes.fut	257
Pony	scenery.pony	252
Vertex Shader File	CC3TexturableRigidBones.vsh	205
Softbridge Basic	greek.sbl	192
Cabal	deeplearning.cabal	180
nuspec	Xamarin.Auth.XamarinForms.nuspec	156
Dockerfile	Dockerfile	152
Mustache	models_list.mustache	141
LOLCODE	LOLTracer.lol	139
BuildStream	astrobib.bst	120
Janet	Janet	101
Cassius	xweek.cassius	94
Docker ignore	.dockerignore	92
Hamlet	upload.hamlet	90
QCL	mod.qcl	88
Dhall	nix.bash.dhall	86
ignore	.ignore	60
Just	Justfile	46
SRecode Template	srecode-test.srt	35
Bitbucket Pipeline	bitbucket-pipelines.yml	30
Ur/Web Project	reader.urp	22
Alchemist	ctrl.crn	16
Zig	main.zig	12
MUMPS	mps	11
Bosque	bosque.bsq	8
Report Definition Language	example.rdl	4
Emacs Dev Env	Project.ede	3
Cargo Lock	Cargo.lock	2
JAI	thekla_atlas.jai	1

How many “pure” projects

Assuming you define pure to mean a project that has 1 language in it. Of course that would not be very interesting by itself, so lets see what the spread is. As it turns out most projects have fewer than 25 languages in them with most in the less than 10 bracket.

The peak in the below graph is for 4 languages.

Of course pure projects might only have one programming language, but have lots of supporting other formats such as markdown, json, yml, css, .gitignore which are picked up by scc. It’s probably reasonable to assume that any project with less than 5 languages is “pure” (for some level of purity) and as it turns out is just over half the total data set. Of course your definition of purity might be different to mine so feel free to adjust to whatever number you like.

What suprises me is an odd bump around 34-35 languages. I have no reasonable explanation as to why this might be the case and it probably warrents some investigation.

The full list of results is included below.

skip table to next section

language count	project count
1	886,559
2	951,009
3	989,025
4	1,070,987
5	1,012,686
6	845,898
7	655,510
8	542,625
9	446,278
10	392,212
11	295,810
12	204,291
13	139,021
14	110,204
15	87,143
16	67,602
17	61,936
18	44,874
19	34,740
20	32,041
21	25,416
22	24,986
23	23,634
24	16,614
25	13,823
26	10,998
27	9,973
28	6,807
29	7,929
30	6,223
31	5,602
32	6,614
33	12,155
34	15,375
35	7,329
36	6,227
37	4,158
38	3,744
39	3,844
40	1,570
41	1,041
42	746
43	1,037
44	1,363
45	934
46	545
47	503
48	439
49	393
50	662
51	436
52	863
53	393
54	684
55	372
56	366
57	842
58	398
59	206
60	208
61	177
62	377
63	450
64	341
65	86
66	78
67	191
68	280
69	61
70	209
71	330
72	171
73	190
74	142
75	102
76	32
77	57
78	50
79	26
80	31
81	63
82	38
83	26
84	72
85	205
86	73
87	67
88	21
89	15
90	6
91	12
92	10
93	8
94	16
95	24
96	7
97	30
98	4
99	1
100	6
101	7
102	16
103	1
104	5
105	1
106	19
108	2
109	2
110	1
111	3
112	1
113	1
114	3
115	5
116	5
118	1
120	5
124	1
125	1
131	2
132	1
134	2
136	1
137	1
138	1
142	1
143	2
144	1
158	1
159	2

Projects with TypeScript but not JavaScript

Ah the modern world of TypeScript. But for projects that are using TypeScipt how many are using TypeScript exclusively?

pure TypeScript projects
27,026 projects

Have to admit, I am a little surprised by that number. While I understand mixing JavaScript with TypeScript is fairly common I would have thought there would be more projects using the new hotness. This may however be mostly down to the projects I was able to pull though and I suspect a refreshed project list with newer projects would change this number drastically.

Anyone using CoffeeScript and TypeScript?

using TypeScript and CoffeeScript
7,849 projects

I have a feeling some TypeScript developers are dry heaving at the very thought of this. If it is of any comfort I suspect most of these projects are things like scc which uses examples of all languages mixed together for testing purposes.

What’s the typical path length, broken up by language

Given that you can either dump all the files you need in a single directory, or span them out using file paths whats the typical path length and number of directories?

This is done by counting the number of path separators / for each file and its location and averaging it out. I didn’t know what to expect here other that I would expect java to be close to the top as its file paths are usually quite deep.

skip table to next section

language	average path length
ABAP	4.406555175781266
ASP	6.372800350189209
ASP.NET	7.25
ATS	4.000007286696899
AWK	4.951896171638623
ActionScript	8.139775436837226
Ada	4.00042700953189
Agda	3.9126438455441743
Alchemist	3.507827758789091
Alex	5.000001311300139
Alloy	5.000488222547574
Android Interface Definition Language	11.0048217363656
Arvo	5.9999994741776135
AsciiDoc	3.5
Assembly	4.75
AutoHotKey	2.2087400984292067
Autoconf	5.8725585937792175
BASH	2.1289059027401294
Basic	3.003903865814209
Batch	6.527053831937014
Bazel	3.18005371087348
Bitbake	2.015624999069132
Bitbucket Pipeline	2.063491820823401
Boo	4.010679721835899
Bosque	4.98316764831543
Brainfuck	4.2025654308963425
BuildStream	3.4058846323741645
C	4.923767089530871
C Header	4.8744963703211965
C Shell	3.027952311891569
C#	3.9303305113013427
C++	3.765686050057411
C++ Header	5.0468749664724015
CMake	4.474763816174707
COBOL	2.718678008809146
CSS	3.158353805542812
CSV	2.0005474090593514
Cabal	2.0234456174658693
Cargo Lock	2.602630615232607
Cassius	3.56445312181134
Ceylon	4.750730359584461
Clojure	3.992209411809762
ClojureScript	4.905477865257108
Closure Template	6.800760253008946
CoffeeScript	4.503051759227674
ColdFusion	6.124976545410084
ColdFusion CFScript	6.188602089623717
Coq	4.000243186950684
Creole	3.124526690922411
Crystal	3.1243934621916196
Cython	5.219657994911814
D	9.291626930357722
Dart	3.939864161220478
Device Tree	6.530643464186369
Dhall	0.12061593477278201
Docker ignore	2.9984694408020562
Dockerfile	3.1281526535752064
Document Type Definition	6.3923129292499254
Elixir	3.9999989270017977
Elm	2.968016967181992
Emacs Dev Env	4.750648772301943
Emacs Lisp	2.0156250001746203
Erlang	4.756546300111156
Expect	5.126907349098477
Extensible Stylesheet Language Transformations	4.519531239055546
F#	5.752862453457055
F*	4.063724638864983
FIDL	4.484130888886213
FORTRAN Legacy	6.117128185927898
FORTRAN Modern	5.742561882347131
Fish	3.993835387425861
Flow9	9.462829245721366
Forth	4.016601327653859
Fragment Shader File	3.8598623261805187
Freemarker Template	11.122007250069213
Futhark	6.188476562965661
GDScript	3.2812499999872675
GLSL	6.6093769371505005
GN	3.497192621218512
Game Maker Language	4.968749999941792
Game Maker Project	3.8828125
Gherkin Specification	3.999099795268081
Go	3.9588454874029275
Go Template	4
Gradle	2.655930499769198
Groovy	11.499969503013528
HEX	3.98394775342058
HTML	4.564478578133282
Hamlet	3.4842224120074867
Handlebars	4.998766578761208
Happy	5.699636149570479
Haskell	2.000140870587468
Haxe	5.999999999999997
IDL	6.249999993495294
Idris	3.515075657458509
Intel HEX	3.983397483825683
Isabelle	4.18351352773584
JAI	7.750007518357038
JSON	3.9999972562254724
JSONL	5.751412352804029
JSX	5.0041952044625715
Jade	4.744544962807595
Janet	3.0312496423721313
Java	11.265740856469563
JavaScript	4.242187985224513
JavaServer Pages	7.999993488161865
Jenkins Buildfile	2.000000000087315
Jinja	6.937498479846909
Julia	3.9999848530092095
Julius	3.187606761406953
Jupyter	2.375
Just	4.312155187124516
Korn Shell	7.0685427486899925
Kotlin	6.455277973786039
LD Script	5.015594720376608
LESS	5.999999999999886
LEX	5.6996263030493495
LOLCODE	3.722656242392418
LaTeX	4.499990686770616
Lean	4.1324310302734375
License	4.7715609660297105
Lisp	6.00048828125
Lua	3.999999057474633
Lucius	3.0000303482974573
Luna	4.758178874869392
MQL Header	5.421851994469764
MQL4	5.171874999953652
MQL5	4.069171198975555
MSBuild	4.8931884765733855
MUMPS	4.999999672174454
Macromedia eXtensible Markup Language	3.9139365140181326
Madlang	3.625
Makefile	4.717208385332443
Mako	4.0349732004106045
Markdown	2.25
Meson	3.342019969206285
Modula3	3.980173215190007
Module-Definition	8.875000973076205
Monkey C	3.0672508481368164
Mustache	6.000003708292297
Nim	3.7500824918105313
Nix	2.0307619677526234
OCaml	3.269392550457269
Objective C	3.526367187490962
Objective C++	5.000000834608569
Opalang	4.500069382134143
Org	5.953919619084296
Oz	4.125
PHP	7.999984720368943
PKGBUILD	4.875488281252839
PSL Assertion	5.004394620715175
Pascal	5.0781240425935845
Patch	3.999999999999819
Perl	4.691352904239976
Plain Text	5.247085583343509
Polly	2.953125
Pony	2.9688720703125
Powershell	4.596205934882159
Processing	3.999931812300937
Prolog	4.4726600636568055
Properties File	3.5139240025278604
Protocol Buffers	6.544742336542192
Puppet	6.662078857422106
PureScript	4.000007774680853
Python	5.4531080610843805
Q#	3.7499999999999996
QCL	2.992309644818306
QML	7.042003512360623
R	3.0628376582587578
Rakefile	4.78515574071335
Razor	8.062499530475186
ReStructuredText	5.061766624473476
Report Definition Language	5.996573380834889
Robot Framework	4.0104638249612155
Ruby	5.1094988621717725
Ruby HTML	5.57654969021678
Rust	3.2265624976654292
SAS	4.826202331129183
SKILL	6.039547920227052
SPDX	4.000203706655157
SQL	7.701822280883789
SRecode Template	3.500030428171159
SVG	5.217570301278483
Sass	6.000000000056957
Scala	4.398563579539738
Scheme	6.999969714792911
Scons	5.010994006631478
Shell	4.988665378738929
Smarty Template	5.000527858268356
Softbridge Basic	4.87873840331963
Specman e	5.765624999999318
Spice Netlist	3.9687499998835882
Standard ML (SML)	4.031283043158929
Stata	6.27345275902178
Stylus	3.5000006667406485
Swift	3
Swig	5.246093751920853
SystemVerilog	2.9995259092956985
Systemd	3.9960937500000284
TCL	2.508188682367951
TOML	2.063069331460588
TaskPaper	2.003804363415667
TeX	3.500000000931251
Thrift	4.956119492650032
Twig Template	8.952746974652655
TypeScript	4.976589231140677
TypeScript Typings	5.832031190521718
Unreal Script	4.22499089783372
Ur/Web	4.41992186196147
Ur/Web Project	5.1147780619789955
V	4.251464832544997
VHDL	4.000000961231823
Vala	3.99804687498741
Varnish Configuration	4.006103516563625
Verilog	3.6906727683381173
Verilog Args File	8.93109059158814
Vertex Shader File	3.8789061926163697
Vim Script	3.9995117782528147
Visual Basic	4.5
Visual Basic for Applications	3.6874962672526417
Vue	7.752930045514701
Wolfram	3.075198844074798
Wren	4
XAML	4.515627968764219
XCode Config	6.969711296260638
XML	6
XML Schema	5.807670593268995
Xtend	4.315674404631856
YAML	3.2037304108964673
Zig	3.4181210184442534
Zsh	2.0616455049940288
gitignore	2.51172685490884
ignore	10.6434326171875
m4	3.7519528857323934
nuspec	4.109375
sed	4.720429063539986

YAML or YML?

Sometime back on the company slack there was a “discussion” with many dying on one hill or the other over the use of .yaml or .yml

The debate can finally(?) be ended. Although I suspect some will still prefer to die on their chosen hill.

extension	count
yaml	3,572,609
yml	14,076,349

Upper lower or mixed case?

What case style is used on filenames? This includes the extension so you would expect it to be mostly mixed case.

style	count
mixed	9,094,732
lower	2,476
upper	2,875

Which of course is not very interesting because generally file extensions are lowercase. What about if we ignore the file extension?

style	count
mixed	8,104,053
lower	347,458
upper	614,922

Not what I would have expected. Mostly mixed is normal, but I would have thought lower would be more popular.

Java Factories

Another one that came up in the internal company slack when looking through some old Java code. I thought why not add a check for any Java code that has Factory, FactoryFactory or FactoryFactoryFactory in the name. The idea being to see how many factories are out there.

type	count	percent
not factory	271,375,574	97.9%
factory	5,695,568	2.09%
factoryfactory	25,316	0.009%
factoryfactoryfactory	0	0%

So slightly over 2% of all the Java code that I checked appeared to be a factory or factoryfactory. Thankfully there are no factoryfactoryfactories and perhaps that joke can finally die, although I am sure at least one non-ironic one exist somewhere in some Java 5 monolith that makes more money every day than I will see over my entire working life.

Ignore files

The .ignore file idea was hammered out by burntsushi and ggreer in a Hacker News thread and is possibly one of the greatest cases of “competing” open source tools working together to a good outcome and done in record time. It has become the defacto way to add things into source control yet have tools ignore them. As it turns out scc also implements .ignore files but counts them as well. Lets see how well the idea has spread.

skip table to next section

.ignore count	project count
0	9,088,796
1	7,848
2	1,258
3	508
4	333
5	43
6	130
7	8
8	14
9	83
10	49
11	35
12	112
13	736
15	4
17	1
18	4
20	2
21	1
23	2
24	3
26	2
27	1
34	31
35	19
36	9
38	2
39	1
43	12
44	1
45	2
46	5
49	7
50	7
51	12
52	2

Future ideas

Id love to do some analysis of tabs vs spaces. Scanning for things like AWS AKIA keys and the like would be pretty neat as well. Id also love to expand out the bitbucket and gitlab coverage and get it broken down via each to see if groups of developers from different camps hang out in different areas.

Shortcomings id love to overcome in the above if I decide to do this again.

• Keeping the URL properly in the metadata somewhere. Using a filename to store this was a bad idea as it was lossy and means it can be hard to identify the file source and location.
• Not bother with S3. There is little point to pay the bandwidth cost when I was only using it for storage. Better to just stuff into the tar file from the beginning.
• Invest some time in learning some tool to help with plotting and charting of results.
• Use a trie or some other data type to keep a full count of filenames rather than the slightly lossy approach I used.
• Add an option to scc to check the type of the file based on keywords as examples such as https://bitbucket.org/abellnets/hrossparser/src/master/xml_files/CIDE.C was picked up as being a C file despite obviously being HTML when the content is inspected. To be fair all code counters I tried behave the same way.
• There appears to be a bug in scc where if a file has no extension but is named as one it will match that file which is incorrect. A bug has been raised in scc to address this https://github.com/boyter/scc/issues/114
• I’d like to add shebang detection into scc https://github.com/boyter/scc/issues/115
• Some sort of check against number of github stars would be pretty neat.
• Analysis against the number of commits would be very interesting.
• I want to add maintainability index calculations at some point. It would be very cool to see what projects are considered the most maintainable based on their size.

So why bother?

Well I can take some of this information and plug it into searchcode.com and scc. Even if only some useful data points. The stated goal was pretty much this and it is potentially very useful to know how your project compares to others. Besides it was a fun way to spend a few days solving some interesting problems. Also I think it is safe to say that scc is a fairly reliable tool at this point.

In addition, I am working on a tool that helps senior-developer or manager types analyze code looking for languages, large files, flaws etc… with the assumption you have to watch multiple repositories. You put in some code and it will tell you how maintainable it is and what skills you need to maintain it. Useful for determining if you should buy or maintain some code-base and getting an overview of what your development team is producing. Should in theory help teams scale through shared resources. Something like AWS Macie but for code is the angle I am working with. It’s something I need for my day job and I suspect others may find use in it, or at least thats the theory.

I should probably put an email sign up for that here at some point to gather interest for that.

Raw / Processed Files

I have included a link to the processed files (20 MB) for those who wish to do their own analysis and corrections. If someone wants to host the raw files to allow others to download it let me know. It is a 83 GB tar.gz file which uncompressed is just over 1 TB in size. It contents consists of just over 9 million JSON files of various sizes.

from Hacker News https://ift.tt/2o5Ixfa