gitlab-org / GitLab_Enterprise

	gitlab npm yarn jest pip gemfile docker
Standard analyses:
		Main Code: 1,279,713 LOC (19875 files) = RB (66%) + JS (19%) + VUE (6%) + SCSS (2%) + HAML (2%) + GRAPHQL (<1%) + XML (<1%) + RAKE (<1%) + EML (<1%) + ERB (<1%) + HTML (<1%) + YAML (<1%) + BUILDER (<1%) + ASC (<1%) + RU (<1%) + PY (<1%) + RUBY (<1%) + META (<1%) + PUB (<1%) Secondary code: Test: 20,038 LOC (351); Generated: 0 LOC (0); Build & Deploy: 1,200 LOC (19); Other: 178,729 LOC (1845);
		File Size: 7% long (>1000 LOC), 63% short (<= 200 LOC)
		Logical Component Decomposition: primary (15 components)
		13 years, 7 months old 100% of code older than 365 days 100% of code not updated in the past 365 days
		38% of code updated more than 50 times Also see temporal dependencies for files frequently changed in same commits.
		Goals: Keep the system simple and easy to change (4)
Experimental analyses (less reliable heuristic analyses):
		Unit Size: 2% long (>100 LOC), 85% short (<= 10 LOC)
		Conditional Complexity: <1% complex (McCabe index > 50), 92% simple (McCabe index <= 5)
		Static Component Dependencies: primary (15 components)

	Features of interest: TODOs 175 files

All Analysis Reports

Source Code Overview

Components

Temporal Dependencies

File Size

File Age & Freshness

File Change Frequency

Commits

Contributors

Unit Size*

Conditional Complexity*

Features of Interest

All Metrics

Goals & Controls

Duplication

Component Dependencies*

Trend

Notes & Findings

Overall Activity Per Year

Latest commit date: 2020-01-21

Reference analysis date: 2025-05-16

0

commits
(30 days)

0

contributors
(30 days)

0	0	0	0	0	1891	36991	34431	33905	24895	9937	6536	4341	2836	643
0	0	0	0	0	274	1034	695	539	610	378	311	317	190	36
2025	2024	2023	2022	2021	2020	2019	2018	2017	2016	2015	2014	2013	2012	2011

Activity Per File Extension

commits

	2025	2024	2023	2022	2021	2020	2019	2018	2017	2016	2015	2014	2013	2012	2011
ru							25	88	22	17	5	12	12	2	3	ru
						55	1965	3231	3973	3466	1310	631	473	560	189	scss
eml						3	17	103	84	76	7					eml
						230	4023	4388	2161							vue
haml						144	4400	6060	6663	6572	3161	1634	1351	1101	338	haml
						403	6076	6746	6650	1318	63	50	85	184	67	js
							10									py
ruby							4	27		9						ruby
						1023	19099	18520	19523	14222	5492	3075	2289	1613	395	rb
asc							3									asc
						3	149	251	315	401	170	56	95	52	49	erb
							2	49								xml
meta									7	6						meta
builder							19	85	98	105	70	13	21	26	6	builder
							44	8	66	84	21	11	5	18	5	html
						37	238	11								graphql
rake						11	389	886	1008	642	347	291	248	149	14	rake
pub							1	13	14	1	2					pub
						617	12300	10700	8890	2385	253	84	44	54	26	yaml

contributors

	2025	2024	2023	2022	2021	2020	2019	2018	2017	2016	2015	2014	2013	2012	2011
ru							11	41	10	9	5	7	5	2	2	ru
						31	205	153	124	136	77	59	40	30	18	scss
eml						3	13	41	36	27	4					eml
						66	271	146	82							vue
haml						67	354	237	205	232	152	107	100	71	24	haml
						75	291	180	134	106	17	19	16	23	10	js
							8									py
ruby							3	19		1						ruby
						175	530	339	293	337	209	167	153	93	22	rb
asc							3									asc
						2	66	74	68	60	28	19	24	15	16	erb
							1	28								xml
meta									6	5						meta
builder							14	39	38	32	20	8	5	10	4	builder
							23	7	30	29	7	6	4	9	3	html
						22	65	9								graphql
rake						9	93	106	96	71	51	38	41	30	7	rake
pub							1	12	12	1	1					pub
						159	550	364	317	180	36	24	11	13	9	yaml

Visual Code Explorers

MAIN (19875)	Circles	Sunburst
TEST (351)	Circles	Sunburst
BUILD AND DEPLOYMENT (19)	Circles	Sunburst
GENERATED (0)	Circles	Sunburst
OTHER (1845)	Circles	Sunburst

File Visualizations

File size views:

File age views:

	files grouped by age category files grouped by folder
	files grouped by freshness category files grouped by folder

File change frequency views:

Contributors per file views:

Contributor Visualizations

Contributor dependency views:

past 30 days: graphviz | 2D graph | 2D graph (with files) | 2D graph (with shared files only) | 3D graph | 3D graph (with files) | 3D graph (with shared files only)
past 3 months: graphviz | 2D graph | 2D graph (with files) | 2D graph (with shared files only) | 3D graph | 3D graph (with files) | 3D graph (with shared files only)
past 6 months: graphviz | 2D graph | 2D graph (with files) | 2D graph (with shared files only) | 3D graph | 3D graph (with files) | 3D graph (with shared files only)
past year: graphviz | 2D graph | 2D graph (with files) | 2D graph (with shared files only) | 3D graph | 3D graph (with files) | 3D graph (with shared files only)

Components and Dependencies Visualizations

Components

Temporal Dependencies

Duplication

Commits Racing Charts

30 days

3 months

6 months

PRIMARY (15)

Bubble Chart

Tree Map

graphviz

2D

3D

graphviz

2D

3D

graphviz

2D

3D

Duplication Graph

All Time

12 Months

File Dependencies Visualizations

Temporal dependencies among files:

past 30 days: no dependencies
past 3 months: no dependencies
past 6 months: no dependencies

Units Visualizations

Unit size and conditional complexity views:

Lists of Files Per Scope

Analysis Results

CONFIGURATION: JSON
ALL ANALYSIS RESULTS: JSON
DUPLICATES: TXT | JSON
UNITS: TXT | JSON
CONTRIBUTORS: TXT | JSON
LOGICAL DECOMPOSITIONS: JSON
CONCERNS: JSON
CONTROLS: TXT
ALL METRICS: TXT

Zipped Files

GIT HISTORY: ZIP
ALL FILES IN ALL ANALYSIS SCOPES: ZIP

Generative AI tools, like ChatGPT or Gemini, can help you explore and discuss various aspects of source code repositories using simple prompts and file uploads. Sokrates provides you with curated data that you can use to analyze your source code further.

Example Prompt 1: Repository Evolution Analyzer (based on git history)

Try on: OpenAI ChatGPT | Claude.ai Chat | Google Gemini

Files to upload: git-history.zip

Prompt:

You are an experienced software architect tasked with analyzing Git commit history.
Context:
- Repository Name: gitlab-org / GitLab_Enterprise_Edition
- Repository Description: GitLab Enterprise Edition
- Data Provided: A plain text file containing Git commit records. Source code is not available.
- Analysis Basis: Your analysis must be based solely on inferring context from the provided file paths, file names, file extensions, commit timestamps, and author information within the commit records.

Input Format:
The commit records are provided in the following format per line:
[YYYY-MM-DD] [author email] [commit hash] [file path changed]

Requested Insights:

Please analyze the provided commit history and generate a report covering the following sections:

1. Management Summary
- Provide a concise, high-level overview suitable for non-technical stakeholders.
- Summarize the repository's likely purpose, main development phases observed, and key takeaways about the team structure/activity.

2. Functional Overview
- Based only on file/module/class names, infer and describe the primary functions and capabilities of the software in this repository.
- What services or features does the codebase seem to support?

3. Key Business/Domain Concepts
- Identify and list core business or domain concepts reflected in the file and directory names (e.g., reporting, analytics, data export, user management, sourcecode analysis, landscape, visualization).

4. Inferred Architecture & Design
- Infer potential architectural patterns (e.g., modular, layered, microservices) based on directory structures and file naming conventions.
- Highlight notable design choices evident from the structure, such as separation of concerns (e.g., common module), use of configuration files, or templating engines.

5. Inferred Technology Stack
- List the programming languages, frameworks, libraries, and tools likely used, based on file extensions (.java, .py, .js, .html, .xml, .yml, pom.xml, dockerfile), configuration files, and common directory structures (e.g., /src/main/java).
- Categorize these technologies where possible (e.g., Backend, Frontend, Build, Infrastructure).

6. Logical Components / Modules
- Identify the major logical components or modules suggested by the directory structure (e.g., codeanalyzer, reports, cli, common, web).
- Briefly describe the inferred purpose of each major component.

7. Evolution by Year
- Summarize the apparent focus of development activity for each year present in the commit history.
- Assign a thematic title to each year (e.g., "Year 1: Foundation & Core Analysis", "Year 2: Reporting & Visualization Expansion").

8. Trends Over Time
Identify and describe significant trends observed across the years, such as:
- Shifts in technology usage (e.g., introduction of Docker).
- Changes in module activity (e.g., increased focus on landscape analysis).
- Signs of project maturity, expansion into new areas, or periods of focused maintenance.
9. Team Dynamics
- Analyze contributor patterns based on author emails and commit frequency:
 - Identify core maintainer(s) vs. occasional contributors.
 - Describe observable contribution trends (e.g., stable team, high turnover, reliance on a single developer).
 - Note any appearance of automated contributors (e.g., Dependabot).
10. Risk Analysis:
- Based on the findings in all previous sections (Team Dynamics, Technology, Architecture, Evolution), identify potential key risks for the project.
- Examples: Key person dependency, technology obsolescence, maintainability challenges due to complexity, potential scalability bottlenecks, bus factor.

Input File:
You will receive the commit records in a plain text file. Please parse this file to perform your analysis.

Example Prompt 2: File name conventions

Try on: OpenAI ChatGPT | Claude.ai Chat | Google Gemini

Files to upload: files.json

Prompt:

Example Prompt 3: Technology analyzer (based of file paths)

Try on: OpenAI ChatGPT | Claude.ai Chat | Google Gemini

Files to upload: files.json

Prompt:

Analyze the provided list of file paths to identify as many technologies, frameworks, tools, programming languages, cloud services, and architectural patterns used in the project as possible.

**Input:**
Please analyze the following list of file structures (provided as a list of strings or a JSON object containing file paths and optionally extensions/metadata):

*(Example: A JSON array like the one previously provided, or a simple list like `["src/server.ts", "Dockerfile", "tests/e2e/spec.cy.js", "infra/main.tf"]`)*

**Analysis Instructions:**
When performing the analysis, please consider the following:

1. **File Extensions:** Identify languages and configuration types (e.g., `.ts`, `.js`, `.py`, `.java`, `.tf`, `.yml`, `.hcl`, `.css`, `.html`).
2. **Directory Structure & Naming:** Look for conventional directory names (`src`, `tests`, `infra`, `docs`, `cicd`) and names indicating specific components or frameworks (`packages/bff`, `features`, `controllers`, `services`, `cypress`).
3. **Specific Filenames:** Identify standard configuration or definition files (e.g., `package.json`, `pom.xml`, `docker-compose.yml`, `Dockerfile`, `Jenkinsfile`, `webpack.config.js`, `tsconfig.json`, `requirements.txt`, `terragrunt.hcl`, `lint-staged.config.js`, `commitlint.config.js`).
4. **Keywords in Paths:** Look for names of known technologies, tools, frameworks, or cloud providers within the paths (e.g., `aws`, `azure`, `lambda`, `sqs`, `react`, `nest`, `k6`, `datadog`, `pm2`).

**Output:**
Please present the findings as a list of identified technologies. If possible, group them into relevant categories such as:

* Programming Languages
* Frameworks & Runtimes
* Testing Tools
* Infrastructure as Code / Cloud Providers
* Containerization & Orchestration
* Build Tools / Bundlers
* Databases (if identifiable)
* Monitoring & Logging
* CI/CD & Dev Tools
* Architectural Patterns