A tool similar to cloc, sloccount and tokei. For counting the lines of code, blank lines, comment lines, and physical lines of source code in many programming languages.
Goal is to be the fastest code counter possible, but also perform COCOMO calculation like sloccount, estimate code complexity similar to cyclomatic complexity calculators and produce unique lines of code or DRYness metrics. In short one tool to rule them all.
Also it has a very short name which is easy to type scc.
If you don't like sloc cloc and code feel free to use the name Succinct Code Counter.
Dual-licensed under MIT or the UNLICENSE.
Using scc commercially? If you want priority support for scc you can purchase a years worth https://boyter.gumroad.com/l/kgenuv which entitles you to priority direct email support from the developer.
If you are comfortable using Go and have >= 1.17 installed:
go install github.com/boyter/scc/v3@latest
or bleeding edge with
go install github.com/boyter/scc@master
A snap install exists thanks to Ricardo.
$ sudo snap install scc
NB Snap installed applications cannot run outside of /home https://askubuntu.com/questions/930437/permission-denied-error-when-running-apps-installed-as-snap-packages-ubuntu-17 so you may encounter issues if you use snap and attempt to run outside this directory.
Or if you have homebrew installed
$ brew install scc
On macOS, you can also install via MacPorts
$ sudo port install scc
Or if you are using Scoop on Windows
$ scoop install scc
Or if you are using Chocolatey on Windows
$ choco install scc
On FreeBSD, scc is available as a package
$ pkg install scc
Or, if you prefer to build from source, you can use the ports tree
$ cd /usr/ports/devel/scc && make install clean
Go to the directory you want to run scc from.
Run the command below to run the latest release of scc on your current working directory:
docker run --rm -it -v "$PWD:/pwd" ghcr.io/lhoupert/scc:master scc /pwd
Binaries for Windows, GNU/Linux and macOS for both i386 and x86_64 machines are available from the releases page.
https://github.com/marketplace/actions/scc-docker-action https://github.com/iRyanBell/scc-docker-action
.github/workflows/main.yml
on: [push]
jobs:
scc_job:
runs-on: ubuntu-latest
name: A job to count the lines of code.
steps:
- name: Checkout
uses: actions/checkout@v3
- name: Get the lines of code.
id: scc
uses: iryanbell/scc-docker-action@v1.0.2
with:
args: ${{ env.workspace }} -i js,go,html,css
https://about.gitlab.com/blog/2023/02/15/code-counting-in-gitlab/
If you would like to assist with getting scc added into apt/chocolatey/etc... please submit a PR or at least raise an issue with instructions.
Read all about how it came to be along with performance benchmarks,
- https://boyter.org/posts/sloc-cloc-code/
- https://boyter.org/posts/why-count-lines-of-code/
- https://boyter.org/posts/sloc-cloc-code-revisited/
- https://boyter.org/posts/sloc-cloc-code-performance/
- https://boyter.org/posts/sloc-cloc-code-performance-update/
Some reviews of scc
- https://nickmchardy.com/2018/10/counting-lines-of-code-in-koi-cms.html
- https://www.feliciano.tech/blog/determine-source-code-size-and-complexity-with-scc/
- https://metaredux.com/posts/2019/12/13/counting-lines.html
A talk given at the first GopherCon AU about scc (press S to see speaker notes)
For performance see the Performance section
Other similar projects,
- SLOCCount the original sloc counter
- cloc, inspired by SLOCCount; implemented in Perl for portability
- gocloc a sloc counter in Go inspired by tokei
- loc rust implementation similar to tokei but often faster
- loccount Go implementation written and maintained by ESR
- ployglot ATS sloc counter
- tokei fast, accurate and written in rust
- sloc coffeescript code counter
Interesting reading about other code counting projects tokei, loc, polyglot and loccount
- https://www.reddit.com/r/rust/comments/59bm3t/a_fast_cloc_replacement_in_rust/
- https://www.reddit.com/r/rust/comments/82k9iy/loc_count_lines_of_code_quickly/
- http://blog.vmchale.com/article/polyglot-comparisons
- http://esr.ibiblio.org/?p=8270
Further reading about processing files on the disk performance
Using scc to process 40 TB of files from Github/Bitbucket/Gitlab
Why use scc?
- It is very fast and gets faster the more CPU you throw at it
- Accurate
- Works very well across multiple platforms without slowdown (Windows, Linux, macOS)
- Large language support
- Can ignore duplicate files
- Has complexity estimations
- You need to tell the difference between Coq and Verilog in the same directory
- cloc yaml output support so potentially a drop in replacement for some users
- Can identify or ignore minified files
- Able to identify many #! files ADVANCED! boyter#115
- Can ignore large files by lines or bytes
- Can calculate the ULOC or unique lines of code by file, language or project
- Supports multiple output formats for integration, csv, sql, json, html and more
Why not use scc?
- You don't like Go for some reason
- It cannot count D source with different nested multi-line comments correctly boyter#27
There are some important differences between scc and other tools that are out there. Here are a few important ones for you to consider.
Blank lines inside comments are counted as comments. While the line is technically blank the decision was made that once in a comment everything there should be considered a comment until that comment is ended. As such the following,
/* blank lines follow
*/
Would be counted as 4 lines of comments. This is noticeable when comparing scc's output to other tools on large repositories.
scc is able to count verbatim strings correctly. For example in C# the following,
private const string BasePath = @"a:\";
// The below is returned to the user as a version
private const string Version = "1.0.0";
Because of the prefixed @ this string ends at the trailing " by ignoring the escape character \ and as such should be counted as 2 code lines and 1 comment. Some tools are unable to deal with this and instead count up to the "1.0.0" as a string which can cause the middle comment to be counted as code rather than a comment.
scc will also tell you the number of bytes it has processed (for most output formats) allowing you to estimate the
cost of running some static analysis tools.
Command line usage of scc is designed to be as simple as possible.
Full details can be found in scc --help or scc -h. Note that the below reflects the state of master not a release, as such
features listed below may be missing from your installation.
Sloc, Cloc and Code. Count lines of code in a directory with complexity estimation.
Version 3.3.4
Ben Boyter <ben@boyter.org> + Contributors
Usage:
scc [flags] [files or directories]
Flags:
--avg-wage int average wage value used for basic COCOMO calculation (default 56286)
--binary disable binary file detection
--by-file display output for every file
-m, --character calculate max and mean characters per line
--ci enable CI output settings where stdout is ASCII
--cocomo-project-type string change COCOMO model type [organic, semi-detached, embedded, "custom,1,1,1,1"] (default "organic")
--count-as string count extension as language [e.g. jsp:htm,chead:"C Header" maps extension jsp to html and chead to C Header]
--count-ignore set to allow .gitignore and .ignore files to be counted
--currency-symbol string set currency symbol (default "$")
--debug enable debug output
-a, --dryness calculate the DRYness of the project (implies --uloc)
--eaf float the effort adjustment factor derived from the cost drivers (1.0 if rated nominal) (default 1)
--exclude-dir strings directories to exclude (default [.git,.hg,.svn])
-x, --exclude-ext strings ignore file extensions (overrides include-ext) [comma separated list: e.g. go,java,js]
-n, --exclude-file strings ignore files with matching names (default [package-lock.json,Cargo.lock,yarn.lock,pubspec.lock,Podfile.lock])
--file-gc-count int number of files to parse before turning the GC on (default 10000)
-f, --format string set output format [tabular, wide, json, json2, csv, csv-stream, cloc-yaml, html, html-table, sql, sql-insert, openmetrics] (default "tabular")
--format-multi string have multiple format output overriding --format [e.g. tabular:stdout,csv:file.csv,json:file.json]
--gen identify generated files
--generated-markers strings string markers in head of generated files (default [do not edit,<auto-generated />])
-h, --help help for scc
-i, --include-ext strings limit to file extensions [comma separated list: e.g. go,java,js]
--include-symlinks if set will count symlink files
-l, --languages print supported languages and extensions
--large-byte-count int number of bytes a file can contain before being removed from output (default 1000000)
--large-line-count int number of lines a file can contain before being removed from output (default 40000)
--min identify minified files
-z, --min-gen identify minified or generated files
--min-gen-line-length int number of bytes per average line for file to be considered minified or generated (default 255)
--no-cocomo remove COCOMO calculation output
-c, --no-complexity skip calculation of code complexity
-d, --no-duplicates remove duplicate files from stats and output
--no-gen ignore generated files in output (implies --gen)
--no-gitignore disables .gitignore file logic
--no-ignore disables .ignore file logic
--no-large ignore files over certain byte and line size set by max-line-count and max-byte-count
--no-min ignore minified files in output (implies --min)
--no-min-gen ignore minified or generated files in output (implies --min-gen)
--no-size remove size calculation output
-M, --not-match stringArray ignore files and directories matching regular expression
-o, --output string output filename (default stdout)
--overhead float set the overhead multiplier for corporate overhead (facilities, equipment, accounting, etc.) (default 2.4)
-p, --percent include percentage values in output
--remap-all string inspect every file and remap by checking for a string and remapping the language [e.g. "-*- C++ -*-":"C Header"]
--remap-unknown string inspect files of unknown type and remap by checking for a string and remapping the language [e.g. "-*- C++ -*-":"C Header"]
--size-unit string set size unit [si, binary, mixed, xkcd-kb, xkcd-kelly, xkcd-imaginary, xkcd-intel, xkcd-drive, xkcd-bakers] (default "si")
--sloccount-format print a more SLOCCount like COCOMO calculation
-s, --sort string column to sort by [files, name, lines, blanks, code, comments, complexity] (default "files")
--sql-project string use supplied name as the project identifier for the current run. Only valid with the --format sql or sql-insert option
-t, --trace enable trace output (not recommended when processing multiple files)
-u, --uloc calculate the number of unique lines of code (ULOC) for the project
-v, --verbose verbose output
--version version for scc
-w, --wide wider output with additional statistics (implies --complexity)
Output should look something like the below for the redis project
$ scc redis
───────────────────────────────────────────────────────────────────────────────
Language Files Lines Blanks Comments Code Complexity
───────────────────────────────────────────────────────────────────────────────
C 296 180267 20367 31679 128221 32548
C Header 215 32362 3624 6968 21770 1636
TCL 143 28959 3130 1784 24045 2340
Shell 44 1658 222 326 1110 187
Autoconf 22 10871 1038 1326 8507 953
Lua 20 525 68 70 387 65
Markdown 16 2595 683 0 1912 0
Makefile 11 1363 262 125 976 59
Ruby 10 795 78 78 639 116
gitignore 10 162 16 0 146 0
YAML 6 711 46 8 657 0
HTML 5 9658 2928 12 6718 0
C++ 4 286 48 14 224 31
License 4 100 20 0 80 0
Plain Text 3 185 26 0 159 0
CMake 2 214 43 3 168 4
CSS 2 107 16 0 91 0
Python 2 219 12 6 201 34
Systemd 2 80 6 0 74 0
BASH 1 118 14 5 99 31
Batch 1 28 2 0 26 3
C++ Header 1 9 1 3 5 0
Extensible Styleshe… 1 10 0 0 10 0
Smarty Template 1 44 1 0 43 5
m4 1 562 116 53 393 0
───────────────────────────────────────────────────────────────────────────────
Total 823 271888 32767 42460 196661 38012
───────────────────────────────────────────────────────────────────────────────
Estimated Cost to Develop (organic) $6,918,301
Estimated Schedule Effort (organic) 28.682292 months
Estimated People Required (organic) 21.428982
───────────────────────────────────────────────────────────────────────────────
Processed 9425137 bytes, 9.425 megabytes (SI)
───────────────────────────────────────────────────────────────────────────────
Note that you don't have to specify the directory you want to run against. Running scc will assume you want to run against the current directory.
You can also run against multiple files or directories scc directory1 directory2 file1 file2 with the results aggregated in the output.
scc mostly supports .ignore files inside directories that it scans. This is similar to how ripgrep, ag and tokei work. .ignore files are 100% the same as .gitignore files with the same syntax, and as such scc will ignore files and directories listed in them. You can add .ignore files to ignore things like vendored dependency checked in files and such. The idea is allowing you to add a file or folder to git and have ignored in the count.
Used inside Intel Nemu Hypervisor to track code changes between revisions https://github.com/intel/nemu/blob/topic/virt-x86/tools/cloc-change.sh#L9 Appears to also be used inside both http://codescoop.com/ https://pinpoint.com/ https://github.com/chaoss/grimoirelab-graal
It also is used to count code and guess language types in https://searchcode.com/ which makes it one of the most frequently run code counters in the world.
You can also hook scc into your gitlab pipeline https://gitlab.com/guided-explorations/ci-cd-plugin-extensions/ci-cd-plugin-extension-scc
Also used by CodeQL boyter#317 and Scaleway https://twitter.com/Scaleway/status/1488087029476995074?s=20&t=N2-z6O-ISDdDzULg4o4uVQ
- https://docs.linuxfoundation.org/lfx/insights/v3-beta-version-current/getting-started/landing-page/cocomo-cost-estimation-simplified
- https://openems.io/
scc uses a small state machine in order to determine what state the code is when it reaches a newline \n. As such it is aware of and able to count
- Single Line Comments
- Multi Line Comments
- Strings
- Multi Line Strings
- Blank lines
Because of this it is able to accurately determine if a comment is in a string or is actually a comment.
It also attempts to count the complexity of code. This is done by checking for branching operations in the code. For example, each of the following for if switch while else || && != == if encountered in Java would increment that files complexity by one.
Let's take a minute to discuss the complexity estimate 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.
Because some languages don't have loops and instead use recursion they can have a lower complexity count. Does this mean they are less complex? Probably not, but the tool cannot see this because it does not build an AST of the code as it only scans through it.
Generally though the complexity there is to help estimate between projects written in the same language, or for finding the most complex file in a project scc --by-file -s complexity which can be useful when you are estimating on how hard something is to maintain, or when looking for those files that should probably be refactored.
As for how it works.
It's my own definition, but tries to be an approximation of cyclomatic complexity https://en.wikipedia.org/wiki/Cyclomatic_complexity although done only on a file level.
The reason it's an approximation is that it's calculated almost for free from a CPU point of view (since its a cheap lookup when counting), whereas a real cyclomatic complexity count would need to parse the code. It gives a reasonable guess in practice though even if it fails to identify recursive methods. The goal was never for it to be exact.
In short when scc is looking through what it has identified as code if it notices what are usually branch conditions it will increment a counter.
The conditions it looks for are compiled into the code and you can get an idea for them by looking at the JSON inside the repository. See https://github.com/boyter/scc/blob/master/languages.json#L3524 for an example of what it's looking at for a file that's Java.
The increment happens for each of the matching conditions and produces the number you see.
ULOC stands for Unique Lines of Code and represents the unique lines across languages, files and the project itself. This idea was taken from
https://cmcenroe.me/2018/12/14/uloc.html where the calculation is presented using standard unix tools sort -u *.h *.c | wc -l. This metric is
there to assist with the estimation of complexity within the project. Quoting the source
In my opinion, the number this produces should be a better estimate of the complexity of a project. Compared to SLOC, not only are blank lines discounted, but so are close-brace lines and other repetitive code such as common includes. On the other hand, ULOC counts comments, which require just as much maintenance as the code around them does, while avoiding inflating the result with license headers which appear in every file, for example.
You can obtain the ULOC by supplying the -u or --uloc argument to scc.
It has a corresponding metric DRYness % which is the percentage of ULOC to CLOC or DRYness = ULOC / SLOC. The
higher the number the more DRY (don't repeat yourself) the project can be considered. In general a higher value
here is a better as it indicates less duplicated code. The DRYness metric was taken from a comment by minimax https://lobste.rs/s/has9r7/uloc_unique_lines_code
To obtain the DRYness metric you can use the -a or --dryness argument to scc, which will implicitly set --uloc.
Note that there is a performance penalty when calculating the ULOC metrics which can double the runtime.
Running the uloc and DRYness calculations against C code a clone of redis produces an output as follows.
$ scc -a -i c redis
───────────────────────────────────────────────────────────────────────────────
Language Files Lines Blanks Comments Code Complexity
───────────────────────────────────────────────────────────────────────────────
C 419 241293 27309 41292 172692 40849
(ULOC) 133535
───────────────────────────────────────────────────────────────────────────────
Total 419 241293 27309 41292 172692 40849
───────────────────────────────────────────────────────────────────────────────
Unique Lines of Code (ULOC) 133535
DRYness % 0.55
───────────────────────────────────────────────────────────────────────────────
Estimated Cost to Develop (organic) $6,035,748
Estimated Schedule Effort (organic) 27.23 months
Estimated People Required (organic) 19.69
───────────────────────────────────────────────────────────────────────────────
Processed 8407821 bytes, 8.408 megabytes (SI)
───────────────────────────────────────────────────────────────────────────────
Futher reading about the ULOC caluclation can be found at https://boyter.org/posts/sloc-cloc-code-new-metic-uloc/
The COCOMO statistics displayed at the bottom of any command line run can be configured as needed.
Estimated Cost to Develop (organic) $664,081
Estimated Schedule Effort (organic) 11.772217 months
Estimated People Required (organic) 5.011633
To change the COCOMO parameters, you can either use one of the default COCOMO models.
scc --cocomo-project-type organic
scc --cocomo-project-type semi-detached
scc --cocomo-project-type embedded
You can also supply your own parameters if you are familiar with COCOMO as follows,
scc --cocomo-project-type "custom,1,1,1,1"
See below for details about how the model choices, and the parameters they use.
Organic – A software project is said to be an organic type if the team size required is adequately small, the problem is well understood and has been solved in the past and also the team members have a nominal experience regarding the problem.
scc --cocomo-project-type "organic,2.4,1.05,2.5,0.38"
Semi-detached – A software project is said to be a Semi-detached type if the vital characteristics such as team-size, experience, knowledge of the various programming environment lie in between that of organic and Embedded. The projects classified as Semi-Detached are comparatively less familiar and difficult to develop compared to the organic ones and require more experience and better guidance and creativity. Eg: Compilers or different Embedded Systems can be considered of Semi-Detached type.
scc --cocomo-project-type "semi-detached,3.0,1.12,2.5,0.35"
Embedded – A software project with requiring the highest level of complexity, creativity, and experience requirement fall under this category. Such software requires a larger team size than the other two models and also the developers need to be sufficiently experienced and creative to develop such complex models.
scc --cocomo-project-type "embedded,3.6,1.20,2.5,0.32"
You can have scc exclude large files from the output.
The option to do so is --no-large which by default will exclude files over 1,000,000 bytes or 40,000 lines.
You can control the size of either value using --large-byte-count or --large-line-count.
For example to exclude files over 1,000 lines and 50kb you could use the following,
scc --no-large --large-byte-count 50000 --large-line-count 1000
You can have scc identify and optionally remove files identified as being minified or generated from the output.
You can do so by enabling the -z flag like so scc -z which will identify any file with an average line byte size >= 255 (by default) as being minified.
Minified files appear like so in the output.
$ scc --no-cocomo -z ./examples/minified/jquery-3.1.1.min.js
───────────────────────────────────────────────────────────────────────────────
Language Files Lines Blanks Comments Code Complexity
───────────────────────────────────────────────────────────────────────────────
JavaScript (min) 1 4 0 1 3 17
───────────────────────────────────────────────────────────────────────────────
Total 1 4 0 1 3 17
───────────────────────────────────────────────────────────────────────────────
Processed 86709 bytes, 0.087 megabytes (SI)
───────────────────────────────────────────────────────────────────────────────
Minified files are indicated with the text (min) after the language name.
Generated files are indicated with the text (gen) after the language name.
You can control the average line byte size using --min-gen-line-length such as scc -z --min-gen-line-length 1. Please note you need -z as modifying this value does not imply minified detection.
You can exclude minified files from the count totally using the flag --no-min-gen. Files which match the minified check will be excluded from the output.
Some files may not have an extension. They will be checked to see if they are a #! file. If they are then the language will be remapped to the correct language. Otherwise, it will not process.
However, you may have the situation where you want to remap such files based on a string inside it. To do so you can use --remap-unknown
scc --remap-unknown "-*- C++ -*-":"C Header"
The above will inspect any file with no extension looking for the string -*- C++ -*- and if found remap the file to be counted using the C Header rules.
You can have multiple remap rules if required,
scc --remap-unknown "-*- C++ -*-":"C Header","other":"Java"
There is also the --remap-all parameter which will remap all files.
Note that in all cases if the remap rule does not apply normal #! rules will apply.
By default scc will output to the console. However you can produce output in other formats if you require.
The different options are tabular, wide, json, csv, csv-stream, cloc-yaml, html, html-table, sql, sql-insert, openmetrics.
Note that you can write scc output to disk using the -o, --output option. This allows you to specify a file to
write your output to. For example scc -f html -o output.html will run scc against the current directory, and output
the results in html to the file output.html.
You can also write to multiple output files, or multiple types to stdout if you want using the --format-multi option. This is
most useful when working in CI/CD systems where you want HTML reports as an artefact while also displaying the counts in stdout.
scc --format-multi "tabular:stdout,html:output.html,csv:output.csv"
The above will run against the current directory, outputting to standard output the default output, as well as writing to output.html and output.csv with the appropriate formats.
This is the default output format when scc is run.
Wide produces some additional information which is the complexity/lines metric. This can be useful when trying to identify the most complex file inside a project based on the complexity estimate.
JSON produces JSON output. Mostly designed to allow scc to feed into other programs.
Note that this format will give you the byte size of every file scc reads allowing you to get a breakdown of the
number of bytes processed.
CSV as an option is good for importing into a spreadsheet for analysis.
Note that this format will give you the byte size of every file scc reads allowing you to get a breakdown of the
number of bytes processed. Also note that CSV respects --by-file and as such will return a summary by default.
csv-stream is an option useful for processing very large repositories where you are likely to run into memory issues. It's output format is 100% the same as CSV.
Note that you should not use this with the format-multi option as it will always print to standard output, and because of how it works will negate the memory saving it normally gains.
savings that this option provides. Note that there is no sort applied with this option.
Is a drop in replacement for cloc using its yaml output option. This is quite often used for passing into other build systems and can help with replacing cloc if required.
$ scc -f cloc-yml processor
# https://github.com/boyter/scc/
header:
url: https://github.com/boyter/scc/
version: 2.11.0
elapsed_seconds: 0.008
n_files: 21
n_lines: 6562
files_per_second: 2625
lines_per_second: 820250
Go:
name: Go
code: 5186
comment: 273
blank: 1103
nFiles: 21
SUM:
code: 5186
comment: 273
blank: 1103
nFiles: 21
$ cloc --yaml processor
21 text files.
21 unique files.
0 files ignored.
---
# http://cloc.sourceforge.net
header :
cloc_url : http://cloc.sourceforge.net
cloc_version : 1.60
elapsed_seconds : 0.196972846984863
n_files : 21
n_lines : 6562
files_per_second : 106.613679608407
lines_per_second : 33314.2364566841
Go:
nFiles: 21
blank: 1137
comment: 606
code: 4819
SUM:
blank: 1137
code: 4819
comment: 606
nFiles: 21
The HTML output options produce a minimal html report using a table that is either standalone html or as just a table html-table
which can be injected into your own HTML pages. The only difference between the two is that the html option includes
html head and body tags with minimal styling.
The markup is designed to allow your own custom styles to be applied. An example report is here to view.
Note that the HTML options follow the command line options, so you can use scc --by-file -f html to produce a report with every
file and not just the summary.
Note that this format if it has the --by-file option will give you the byte size of every file scc reads allowing you to get a breakdown of the
number of bytes processed.
The SQL output format "mostly" compatible with cloc's SQL output format https://github.com/AlDanial/cloc#sql-
While all queries on the cloc documentation should work as expected, you will not be able to append output from scc and cloc into the same database. This is because the table format is slightly different
to account for scc including complexity counts and bytes.
The difference between sql and sql-insert is that sql will include table creation while the latter will only have the insert commands.
Usage is 100% the same as any other scc command but sql output will always contain per file details. You can compute totals yourself using SQL, however COCOMO calculations will appear against the metadata table as the columns estimated_cost estimated_schedule_months and estimated_people.
The below will run scc against the current directory, name the output as the project scc and then pipe the output to sqlite to put into the database code.db
scc --format sql --sql-project scc . | sqlite3 code.db
Assuming you then wanted to append another project
scc --format sql-insert --sql-project redis . | sqlite3 code.db
You could then run SQL against the database,
sqlite3 code.db 'select project,file,max(nCode) as nL from t
group by project order by nL desc;'
See the cloc documentation for more examples.
OpenMetrics is a metric reporting format specification extending the Prometheus exposition text format.
The produced output is natively supported by Prometheus and GitLab CI
Note that OpenMetrics respects --by-file and as such will return a summary by default.
The output includes a metadata header containing definitions of the returned metrics:
# TYPE scc_files count
# HELP scc_files Number of sourcecode files.
# TYPE scc_lines count
# UNIT scc_lines lines
# HELP scc_lines Number of lines.
# TYPE scc_code count
# HELP scc_code Number of lines of actual code.
# TYPE scc_comments count
# HELP scc_comments Number of comments.
# TYPE scc_blanks count
# HELP scc_blanks Number of blank lines.
# TYPE scc_complexity count
# HELP scc_complexity Code complexity.
# TYPE scc_bytes count
# UNIT scc_bytes bytes
# HELP scc_bytes Size in bytes.
The header is followed by the metric data in either language summary form:
scc_files{language="Go"} 1
scc_lines{language="Go"} 1000
scc_code{language="Go"} 1000
scc_comments{language="Go"} 1000
scc_blanks{language="Go"} 1000
scc_complexity{language="Go"} 1000
scc_bytes{language="Go"} 1000
or, if --by-file is present, in per file form:
scc_lines{language="Go",file="./bbbb.go"} 1000
scc_code{language="Go",file="./bbbb.go"} 1000
scc_comments{language="Go",file="./bbbb.go"} 1000
scc_blanks{language="Go",file="./bbbb.go"} 1000
scc_complexity{language="Go",file="./bbbb.go"} 1000
scc_bytes{language="Go",file="./bbbb.go"} 1000
Generally scc will the fastest code counter compared to any I am aware of and have compared against. The below comparisons are taken from the fastest alternative counters. See Other similar projects above to see all of the other code counters compared against. It is designed to scale to as many CPU's cores as you can provide.
However if you want greater performance and you have RAM to spare you can disable the garbage collector like the following on linux GOGC=-1 scc . which should speed things up considerably. For some repositories turning off the code complexity calculation via -c can reduce runtime as well.
Benchmarks are run on fresh 32 Core CPU Optimised Digital Ocean Virtual Machine 2022/09/20 all done using hyperfine with 3 warm-up runs and 10 timed runs.
scc v3.1.0
tokei v12.1.2
loc v0.5.0
polyglot v0.5.29
See https://github.com/boyter/scc/blob/master/benchmark.sh to see how the benchmarks are run.
Benchmark 1: scc redis
Time (mean ± σ): 20.2 ms ± 1.7 ms [User: 127.1 ms, System: 47.0 ms]
Range (min … max): 16.8 ms … 25.8 ms 132 runs
Benchmark 2: scc -c redis
Time (mean ± σ): 17.0 ms ± 1.4 ms [User: 91.6 ms, System: 32.7 ms]
Range (min … max): 14.3 ms … 21.6 ms 169 runs
Benchmark 3: tokei redis
Time (mean ± σ): 33.7 ms ± 5.0 ms [User: 246.4 ms, System: 55.0 ms]
Range (min … max): 24.2 ms … 47.5 ms 76 runs
Benchmark 4: loc redis
Time (mean ± σ): 36.9 ms ± 30.6 ms [User: 756.5 ms, System: 20.7 ms]
Range (min … max): 9.9 ms … 123.9 ms 71 runs
Benchmark 5: polyglot redis
Time (mean ± σ): 21.8 ms ± 0.9 ms [User: 32.1 ms, System: 46.3 ms]
Range (min … max): 20.0 ms … 28.4 ms 138 runs
Summary
'scc -c redis' ran
1.19 ± 0.14 times faster than 'scc redis'
1.28 ± 0.12 times faster than 'polyglot redis'
1.98 ± 0.33 times faster than 'tokei redis'
2.17 ± 1.81 times faster than 'loc redis'Benchmark 1: scc cpython
Time (mean ± σ): 52.6 ms ± 3.8 ms [User: 624.3 ms, System: 121.5 ms]
Range (min … max): 45.3 ms … 62.3 ms 47 runs
Benchmark 2: scc -c cpython
Time (mean ± σ): 46.0 ms ± 3.8 ms [User: 468.0 ms, System: 111.2 ms]
Range (min … max): 40.0 ms … 58.0 ms 67 runs
Benchmark 3: tokei cpython
Time (mean ± σ): 110.4 ms ± 6.6 ms [User: 1239.8 ms, System: 114.5 ms]
Range (min … max): 98.3 ms … 123.6 ms 26 runs
Benchmark 4: loc cpython
Time (mean ± σ): 52.9 ms ± 25.2 ms [User: 1103.0 ms, System: 57.4 ms]
Range (min … max): 30.0 ms … 118.9 ms 49 runs
Benchmark 5: polyglot cpython
Time (mean ± σ): 82.4 ms ± 3.0 ms [User: 153.3 ms, System: 168.8 ms]
Range (min … max): 74.8 ms … 88.7 ms 36 runs
Summary
'scc -c cpython' ran
1.14 ± 0.13 times faster than 'scc cpython'
1.15 ± 0.56 times faster than 'loc cpython'
1.79 ± 0.16 times faster than 'polyglot cpython'
2.40 ± 0.24 times faster than 'tokei cpython'Linux Kernel https://github.com/torvalds/linux
Benchmark 1: scc linux
Time (mean ± σ): 743.0 ms ± 18.8 ms [User: 17133.4 ms, System: 1280.2 ms]
Range (min … max): 709.4 ms … 778.8 ms 10 runs
Benchmark 2: scc -c linux
Time (mean ± σ): 528.8 ms ± 11.8 ms [User: 10272.0 ms, System: 1236.9 ms]
Range (min … max): 508.9 ms … 543.1 ms 10 runs
Benchmark 3: tokei linux
Time (mean ± σ): 736.5 ms ± 18.2 ms [User: 13098.3 ms, System: 2276.0 ms]
Range (min … max): 699.3 ms … 760.8 ms 10 runs
Benchmark 4: loc linux
Time (mean ± σ): 567.1 ms ± 113.4 ms [User: 15984.5 ms, System: 1037.0 ms]
Range (min … max): 381.8 ms … 656.3 ms 10 runs
Benchmark 5: polyglot linux
Time (mean ± σ): 1.241 s ± 0.027 s [User: 2.973 s, System: 2.636 s]
Range (min … max): 1.196 s … 1.299 s 10 runs
Summary
'scc -c linux' ran
1.07 ± 0.22 times faster than 'loc linux'
1.39 ± 0.05 times faster than 'tokei linux'
1.41 ± 0.05 times faster than 'scc linux'
2.35 ± 0.07 times faster than 'polyglot linux'If you enable duplicate detection expect performance to fall by about 20% in scc.
Performance is tracked for some releases and presented below.
https://jsfiddle.net/m1w7kgqv/
Some CI/CD systems which will remain nameless do not work very well with the box-lines used by scc. To support those systems better there is an option --ci which will change the default output to ASCII only.
$ scc --ci main.go
-------------------------------------------------------------------------------
Language Files Lines Blanks Comments Code Complexity
-------------------------------------------------------------------------------
Go 1 272 7 6 259 4
-------------------------------------------------------------------------------
Total 1 272 7 6 259 4
-------------------------------------------------------------------------------
Estimated Cost to Develop $6,539
Estimated Schedule Effort 2.268839 months
Estimated People Required 0.341437
-------------------------------------------------------------------------------
Processed 5674 bytes, 0.006 megabytes (SI)
-------------------------------------------------------------------------------
The --format-multi option is especially useful in CI/CD where you want to get multiple output formats useful for storage or reporting.
If you want to hack away feel free! PR's are accepted. Some things to keep in mind. If you want to change a language definition you need to update languages.json and then run go generate which will convert it into the processor/constants.go file.
For all other changes ensure you run all tests before submitting. You can do so using go test ./.... However for maximum coverage please run test-all.sh which will run gofmt, unit tests, race detector and then all of the integration tests. All of those must pass to ensure a stable release.
The core part of scc which is the counting engine is exposed publicly to be integrated into other Go applications. See https://github.com/pinpt/ripsrc for an example of how to do this.
It also powers all of the code calculations displayed in https://searchcode.com/ such as https://searchcode.com/file/169350674/main.go/ making it one of the more used code counters in the world.
However as a quick start consider the following,
Note that you must pass in the number of bytes in the content in order to ensure it is counted!
package main
import (
"fmt"
"io/ioutil"
"github.com/boyter/scc/v3/processor"
)
type statsProcessor struct{}
func (p *statsProcessor) ProcessLine(job *processor.FileJob, currentLine int64, lineType processor.LineType) bool {
switch lineType {
case processor.LINE_BLANK:
fmt.Println(currentLine, "lineType", "BLANK")
case processor.LINE_CODE:
fmt.Println(currentLine, "lineType", "CODE")
case processor.LINE_COMMENT:
fmt.Println(currentLine, "lineType", "COMMENT")
}
return true
}
func main() {
bts, _ := ioutil.ReadFile("somefile.go")
t := &statsProcessor{}
filejob := &processor.FileJob{
Filename: "test.go",
Language: "Go",
Content: bts,
Callback: t,
Bytes: int64(len(bts)),
}
processor.ProcessConstants() // Required to load the language information and need only be done once
processor.CountStats(filejob)
}
To add or modify a language you will need to edit the languages.json file in the root of the project, and then run go generate to build it into the application. You can then go install or go build as normal to produce the binary with your modifications.
Its possible that you may see the counts vary between runs. This usually means one of two things. Either something is changing or locking the files under scc, or that you are hitting ulimit restrictions. To change the ulimit see the following links.
- https://superuser.com/questions/261023/how-to-change-default-ulimit-values-in-mac-os-x-10-6#306555
- https://unix.stackexchange.com/questions/108174/how-to-persistently-control-maximum-system-resource-consumption-on-mac/221988#221988
- https://access.redhat.com/solutions/61334
- https://serverfault.com/questions/356962/where-are-the-default-ulimit-values-set-linux-centos
- https://www.tecmint.com/increase-set-open-file-limits-in-linux/
To help identify this issue run scc like so scc -v . and look for the message too many open files in the output. If it is there you can rectify it by setting your ulimit to a higher value.
If you are running scc in a low memory environment < 512 MB of RAM you may need to set --file-gc-count to a lower value such as 0 to force the garbage collector to be on at all times.
A sign that this is required will be scc crashing with panic errors.
scc is pretty well tested with many unit, integration and benchmarks to ensure that it is fast and complete.
Packaging as of version v3.1.0 is done through https://goreleaser.com/
Note if you plan to run scc in Alpine containers you will need to build with CGO_ENABLED=0.
See the below dockerfile as an example on how to achieve this based on this issue boyter#208
FROM golang as scc-get
ENV GOOS=linux \
GOARCH=amd64 \
CGO_ENABLED=0
ARG VERSION
RUN git clone --branch $VERSION --depth 1 https://github.com/boyter/scc
WORKDIR /go/scc
RUN go build -ldflags="-s -w"
FROM alpine
COPY --from=scc-get /go/scc/scc /bin/
ENTRYPOINT ["scc"]
You can use scc to provide badges on your github/bitbucket/gitlab/sr.ht open repositories. For example,
The format to do so is,
https://sloc.xyz/PROVIDER/USER/REPO
An example of the badge for scc is included below, and is used on this page.
[](https://github.com/boyter/scc/)
By default the badge will show the repo's lines count. You can also specify for it to show a different category, by using the ?category= query string.
Valid values include code, blanks, lines, comments, cocomo and examples of the appearance are included below.
For cocomo you can also set the avg-wage value similar to scc itself. For example,
https://sloc.xyz/github/boyter/scc/?category=cocomo&avg-wage=1 https://sloc.xyz/github/boyter/scc/?category=cocomo&avg-wage=100000
Note that the avg-wage value must be a positive integer otherwise it will revert back to the default value of 56286.
NB it may not work for VERY large repositories (has been tested on Apache hadoop/spark without issue).
You can find the source code for badges in the repository at https://github.com/boyter/scc/blob/master/cmd/badges/main.go
- github - https://sloc.xyz/github/boyter/scc/
- sr.ht - https://sloc.xyz/sr.ht/~nektro/magnolia-desktop/
- bitbucket - https://sloc.xyz/bitbucket/boyter/decodingcaptchas
- gitlab - https://sloc.xyz/gitlab/esr/loccount
List of supported languages. The master version of scc supports 239 languages at last count. Note that this is always assumed that you built from master, and it might trail behind what is actually supported. To see what your version of scc supports run scc --languages