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Sloc, Cloc and Code: scc is a very fast accurate code counter with complexity calculations and COCOMO estimates written in pure Go

License: MIT License

Go 90.76% PowerShell 4.92% Shell 4.32%

scc's Introduction

Sloc Cloc and Code (scc)

scc

A tool similar to cloc, sloccount and tokei. For counting physical 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 and to estimate code complexity similar to cyclomatic complexity calculators. 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.

Build Status Go Report Card Coverage Status Scc Count Badge Mentioned in Awesome Go

Dual-licensed under MIT or the UNLICENSE.

Read all about how it came to be along with performance benchmarks,

Some reviews of scc

For performance see the Performance section

Other similar projects,

  • cloc the original sloc counter
  • 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
  • sloccount written as a faster cloc
  • tokei fast, accurate and written in rust

Interesting reading about other code counting projects tokei, loc, polyglot and loccount

Further reading about processing files on the disk performance

Install

If you are comfortable using Go and have >= 1.10 installed the usual go get -u github.com/boyter/scc/ will install for you.

If you are looking for binaries see Releases for Windows, GNU/Linux and macOS for both i386 and x86_64 bit machines.

A snap install exists https://snapcraft.io/scc thanks to Ricardo. You can run sudo snap install scc to install it.

If you would like to assist with getting scc added into apt/homebrew/chocolatey/etc... please submit a PR or at least raise an issue with instructions.

Pitch

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

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

Usage

Command line usage of scc is designed to be as simple as possible. Full details can be found in scc --help or scc -h.

Sloc, Cloc and Code. Count lines of code in a directory with complexity estimation.
Ben Boyter <[email protected]> + Contributors

Usage:
  scc [flags]

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
      --ci                      enable CI output settings where stdout is ASCII
      --debug                   enable debug output
      --exclude-dir strings     directories to exclude (default [.git,.hg,.svn])
      --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, csv, cloc-yaml] (default "tabular")
  -h, --help                    help for scc
  -i, --include-ext strings     limit to file extensions [comma separated list: e.g. go,java,js]
  -l, --languages               print supported languages and extensions
      --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-gitignore            disables .gitignore file logic
      --no-ignore               disables .ignore file logic
  -M, --not-match stringArray   ignore files and directories matching regular expression
  -o, --output string           output filename (default stdout)
  -s, --sort string             column to sort by [files, name, lines, blanks, code, comments, complexity] (default "files")
  -t, --trace                   enable trace output. Not recommended when processing multiple files
  -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 .
───────────────────────────────────────────────────────────────────────────────
Language                 Files     Lines   Blanks  Comments     Code Complexity
───────────────────────────────────────────────────────────────────────────────
C                          258    153080    17005     26121   109954      27671
C Header                   200     28794     3252      5877    19665       1557
TCL                        101     17802     1879       981    14942       1439
Shell                       36      1109      133       252      724        118
Lua                         20       525       68        70      387         65
Autoconf                    18     10821     1026      1326     8469        951
Makefile                    10      1082      220       103      759         51
Ruby                        10       778       78        71      629        115
Markdown                     9      1935      527         0     1408          0
gitignore                    9       120       16         0      104          0
HTML                         5      9658     2928        12     6718          0
C++                          4       286       48        14      224         31
License                      4       100       20         0       80          0
YAML                         4       266       20         3      243          0
CSS                          2       107       16         0       91          0
Python                       2       219       39        18      162         68
Batch                        1        28        2         0       26          3
C++ Header                   1         9        1         3        5          0
Extensible Styleshe…         1        10        0         0       10          0
Plain Text                   1        23        7         0       16          0
Smarty Template              1        44        1         0       43          5
m4                           1       562      116        53      393          0
───────────────────────────────────────────────────────────────────────────────
Total                      698    227358    27402     34904   165052      32074
───────────────────────────────────────────────────────────────────────────────
Estimated Cost to Develop $5,755,686
Estimated Schedule Effort 29.835114 months
Estimated People Required 22.851995
───────────────────────────────────────────────────────────────────────────────

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.

Interesting Use Cases

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/ and https://pinpoint.com/

Features

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.

Complexity Estimates

Lets 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.

Performance

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 CPU Optimised Digital Ocean Virtual Machine 2019/03/04 all done using hyperfine with 3 warm-up runs and 10 timed runs.

scc v2.2.0 (compiled with Go 1.12)
tokei v9.0.0 (compiled with Rust 1.33)
loc v0.5.0 (compiled with Rust 1.33)
polyglot v0.5.19 (downloaded from github)
Program Runtime
scc 24.0 ms ± 2.7 ms
scc (no complexity) 18.9 ms ± 2.2 ms
tokei 26.6 ms ± 3.3 ms
loc 80.1 ms ± 54.7 ms
polyglot 15.0 ms ± 1.1 ms
Program Runtime
scc 64.3 ms ± 6.3 ms
scc (no complexity) 53.8 ms ± 6.5 ms
tokei 74.9 ms ± 11.6 ms
loc 155.1 ms ± 58.9 ms
polyglot 83.9 ms ± 9.4 ms
Program Runtime
scc 537.3 ms ± 33.1 ms
scc (no complexity) 438.9 ms ± 30.3 ms
tokei 525.9 ms ± 32.7 ms
loc 1.543 s ± 0.059 s
polyglot 1.022 s ± 0.056 s

If you enable duplicate detection expect performance to fall by about 50%

CI/CD Support

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       171        6         4      161          2
-------------------------------------------------------------------------------
Total                        1       171        6         4      161          2
-------------------------------------------------------------------------------
Estimated Cost to Develop $3,969
Estimated Schedule Effort 1.876811 months
Estimated People Required 0.250551
-------------------------------------------------------------------------------

API Support

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. However as a quick start consider the following,

package main

import (
	"fmt"
	"io/ioutil"

	"github.com/boyter/scc/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,
	}

	processor.ProcessConstants() // Required to load the language information and need only be done once
	processor.CountStats(filejob)
}

Adding/Modifying Languages

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.

Issues

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.

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.

Low Memory

If you are running scc in a low memory environment < 512 MB of RAM you may need to set --filegccount or --fgc 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.

Tests

scc is pretty well tested with many unit, integration and benchmarks to ensure that it is fast and complete.

Package

Run go build for windows and linux then the following in linux, keep in mind need to update the version

GOOS=darwin GOARCH=amd64 go build -ldflags="-s -w" && zip -r9 scc-2.7.0-x86_64-apple-darwin.zip scc
GOOS=darwin GOARCH=386 go build -ldflags="-s -w" && zip -r9 scc-2.7.0-i386-apple-darwin.zip scc
GOOS=windows GOARCH=amd64 go build -ldflags="-s -w" && zip -r9 scc-2.7.0-x86_64-pc-windows.zip scc.exe
GOOS=windows GOARCH=386 go build -ldflags="-s -w" && zip -r9 scc-2.7.0-i386-pc-windows.zip scc.exe
GOOS=linux GOARCH=amd64 go build -ldflags="-s -w" && zip -r9 scc-2.7.0-x86_64-unknown-linux.zip scc
GOOS=linux GOARCH=386 go build -ldflags="-s -w" && zip -r9 scc-2.7.0-i386-unknown-linux.zip scc

Badges (beta)

You can use scc to provide badges on your github/bitbucket/gitlab open repositories. For example, Scc Count Badge 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.

[![Scc Count Badge](https://sloc.xyz/github/boyter/scc/)](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.

Scc Count Badge Scc Count Badge Scc Count Badge Scc Count Badge Scc Count Badge

NB it may not work for VERY large repositories (has been tested on Apache hadoop/spark without issue).

Languages

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

Click here to view all languages supported by master

scc's People

Contributors

boyter avatar dbaggerman avatar pombredanne avatar righolt avatar walter-weinmann avatar elindsey avatar nemith avatar lukas-brenning avatar rmg avatar rtennill avatar apocelipes avatar anup1920sri avatar mcanfield avatar mndrix avatar rbradford avatar robertrossmann avatar

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