azure / azure-sdk-for-java

865	3037	2932	3065	3770	3412	1822	1023	2699	4637	2362	675	760	790	385
69	128	170	157	179	143	95	84	72	96	53	20	17	19	11
2025	2024	2023	2022	2021	2020	2019	2018	2017	2016	2015	2014	2013	2012	2011

	travis github actions maven npm docker helm
Standard analyses:
		Main Code: 5,881,626 LOC (63469 files) = JAVA (99%) + PS1 (<1%) + SCALA (<1%) + XML (<1%) + PY (<1%) + YAML (<1%) + HTML (<1%) + JS (<1%) + CSS (<1%) + PSM1 (<1%) + IPYNB (<1%) + CMD (<1%) + IN (<1%) + XSL (<1%) + JSP (<1%) + GROOVY (<1%) + DOCKERIGNORE (<1%) + PUB (<1%) Secondary code: Test: 736,044 LOC (4755); Generated: 634,886 LOC (26029); Build & Deploy: 50,106 LOC (778); Other: 821,318 LOC (15730);
		File Size: 8% long (>1000 LOC), 58% short (<= 200 LOC)
		Logical Component Decomposition: primary (3 components)
		13 years, 7 months old 86% of code older than 365 days 14% of code not updated in the past 365 days
		<1% 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: <1% long (>100 LOC), 76% short (<= 10 LOC)
		Conditional Complexity: 1% complex (McCabe index > 50), 80% simple (McCabe index <= 5)
		Static Component Dependencies: primary (3 components)

	Features of interest: TODOs 372 files

All Analysis Reports

Source Code Overview

Components

Temporal Dependencies

Duplication

File Size

File Age & Freshness

File Change Frequency

Commits

Contributors

Unit Size*

Conditional Complexity*

Features of Interest

All Metrics

Goals & Controls

Component Dependencies*

Trend

Notes & Findings

Overall Activity Per Year

Latest commit date: 2025-05-09

Reference analysis date: 2025-05-09

205

commits
(30 days)

36

contributors
(30 days)

865	3037	2932	3065	3770	3412	1822	1023	2699	4637	2362	675	760	790	385
69	128	170	157	179	143	95	84	72	96	53	20	17	19	11
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
	379	1551	1542	1327	1877	2060	1168	718	2238	3208	650	543	681	734	340	java
	415	1482	1352	1416	1566	1197	726	319	533	591	184	184	161	73	43	xml
	185	507	327	371	530	501	238	30	105	132	57					yaml
	96	182	126	198	260	178	15		6	7	52					ps1
	21	57	40	79	89	67	31	6	10	360	35					py
	8	47	43	55	108	9										scala
	3	6	10	9	27	34	7	8	110	521	544					js
		4	1	5	15											ipynb
in		2		1						18						in
	1	1				1	15									dockerignore
		1	31	70	130	146	199	194			4	49				groovy
cmd		1	1		5	4	4		3		27	10				cmd
			1		6	6	1	4		1	9					css
						2			1	5						jsp
xsl				1												xsl
			1			11										psm1
			2	2	22	19	11	13	6	12	41	48	60	18	10	html
pub						2										pub

contributors

	2025	2024	2023	2022	2021	2020	2019	2018	2017	2016	2015	2014	2013	2012	2011
	53	99	133	117	140	108	76	66	65	66	33	17	16	18	11	java
	36	64	80	84	93	74	69	54	49	46	26	14	15	14	8	xml
	32	56	51	54	66	56	40	14	20	27	13					yaml
	9	20	15	14	29	22	6		3	5	11					ps1
	4	11	12	14	21	18	10	5	6	30	5					py
	2	4	8	11	6	2										scala
	3	2	2	2	10	10	7	6	21	38	24					js
		2	1	4	2											ipynb
in		2		1						8						in
	1	1				1	1									dockerignore
		1	5	6	13	7	22	6			4	6				groovy
cmd		1	1		2	4	4		3		12	2				cmd
			1		3	5	1	2		1	6					css
						2			1	3						jsp
xsl				1												xsl
			1			1										psm1
			2	2	13	12	8	5	4	5	13	5	5	6	6	html
pub						2										pub

Visual Code Explorers

MAIN (63469)	Circles	Sunburst
TEST (4755)	Circles	Sunburst
BUILD AND DEPLOYMENT (778)	Circles	Sunburst
GENERATED (26029)	Circles	Sunburst
OTHER (15730)	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 (3)

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: graphviz | 2D graph | 2D graph (with commits) | 3D graph | 3D graph (with commits)
past 3 months: graphviz | 2D graph | 2D graph (with commits) | 3D graph | 3D graph (with commits)
past 6 months: graphviz | 2D graph | 2D graph (with commits) | 3D graph | 3D graph (with commits)

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: azure / azure-sdk-for-java
- Repository Description: This repository is for active development of the Azure SDK for Java. For consumers of the SDK we recommend visiting our public developer docs at https://docs.microsoft.com/java/azure/ or our versioned developer docs at https://azure.github.io/azure-sdk-for-java. 
- 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