GitHub - Zeeeepa/analyzer: analyzer

https://pypi.org/project/archex/ archex is a Python library and CLI that transforms any Git repository into structured architectural intelligence and token-budget-aware code context. It serves two consumers from a single index: human architects receive an ArchProfile with module boundaries, dependency graphs, detected patterns, and interface surfaces; AI agents receive a ContextBundle with relevance-ranked, syntax-aligned code chunks assembled to fit within a specified token budget.

https://pypi.org/project/sari/ LSP-first local indexing/search engine + MCP daemon.

CodeAnalysis - super code analysis +Indexing, Embedding, RAG, Reranking, AST, Tree-sitter, Analysis for context Q&A, Pattern-learning+ linting + embedding + storage + auto-resolve + index + RAG + Q&A + runtime error showing + lsp error +type errors + deadcode + entrypointfind+variableusefind+ PATTERNLearning + PatternMining + PatternLibrary + FunctionLibrary + FeatureLibrary + CodeWarehouse

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===================================================== CODE QUALITY ANALYZER & AUTOMATED TESTING DASHBOARD

ABSTRACT

Software quality plays a critical role in the reliability and maintainability of modern applications. Manual code reviews and testing processes are often time-consuming, error-prone, and inefficient. This project, titled “Code Quality Analyzer and Automated Testing Dashboard”, aims to provide an automated solution for analyzing source code quality and executing test cases while presenting the results through an interactive dashboard.

The system performs static code analysis to detect bugs, code smells, complexity issues, and violations of coding standards. It also runs automated tests to evaluate correctness and test coverage. The generated results are stored and visualized using charts and reports, enabling developers to quickly assess the health of their projects. The proposed system improves productivity, ensures better code quality, and supports informed decision-making in software development.

CHAPTER 1: INTRODUCTION

1.1 Overview In today’s software development environment, maintaining high code quality is essential for building scalable, secure, and reliable applications. As projects grow in size and complexity, manual code review and testing become inefficient and prone to human error. Automated tools for code analysis and testing help developers identify issues early in the development lifecycle. The Code Quality Analyzer and Automated Testing Dashboard provides a centralized platform that automatically evaluates code quality and executes test cases while presenting insights through an easy-to-understand dashboard.

1.2 Problem Statement Manual code analysis and testing require significant time and expertise, making it difficult to maintain consistent quality standards across projects. Existing tools often focus on either code analysis or testing, lacking integration and centralized visualization. There is a need for a unified system that automates code quality checks, test execution, and result visualization in a single platform.

1.3 Objectives of the Project

To analyze source code quality using static analysis techniques To automate the execution of test cases To visualize quality metrics and test results through a dashboard To generate detailed reports for developers To improve software reliability and maintainability 1.4 Scope of the Project The project supports automated code quality analysis and testing for selected programming languages. It focuses on static analysis, unit testing, and result visualization. Advanced security analysis and large-scale enterprise integrations are outside the scope of the current version.

1.5 Applications

Software development teams Educational institutions Continuous Integration and Continuous Deployment (CI/CD) environments Code quality assessment for academic projects CHAPTER 2: LITERATURE SURVEY

2.1 Existing Systems Existing systems include manual code reviews, standalone static analysis tools, and independent testing frameworks. Tools such as linters and test runners are commonly used but operate independently without unified visualization.

2.2 Limitations of Existing Systems

Lack of integration between code analysis and testing Limited visualization and reporting features Manual interpretation of results Difficulty in tracking quality trends over time 2.3 Proposed System The proposed system integrates code quality analysis and automated testing into a single platform. It provides a centralized dashboard that displays actionable insights, historical trends, and detailed reports, improving efficiency and decision-making.

CHAPTER 3: SYSTEM REQUIREMENTS

3.1 Functional Requirements

User authentication and project management Upload source code or connect a repository Perform static code quality analysis Execute automated test cases Display results on a dashboard Generate downloadable reports 3.2 Non-Functional Requirements

High performance and responsiveness Scalability to handle multiple projects Secure handling of source code Reliable and accurate analysis User-friendly interface 3.3 Hardware Requirements

Processor: Intel i3 or higher RAM: 8 GB minimum Storage: 20 GB free disk space 3.4 Software Requirements

Operating System: Windows / Linux Programming Languages: Python / JavaScript Frontend Framework: React / HTML / CSS Backend Framework: Flask / Node.js Database: MySQL / MongoDB CHAPTER 4: SYSTEM DESIGN

4.1 System Architecture The system follows a client-server architecture where the frontend interacts with the backend through APIs. The backend coordinates code analysis, test execution, data storage, and report generation.

4.2 Module Description

4.2.1 Code Quality Analyzer Module Analyzes source code to identify bugs, complexity issues, code smells, and violations of coding standards.

4.2.2 Automated Testing Module Executes predefined test cases automatically and records pass/fail results and execution time.

4.2.3 Dashboard Module Displays quality metrics, test results, and trends using charts and graphs.

4.2.4 Reporting Module Generates detailed reports summarizing analysis results and recommendations.

4.2.5 User Management Module Handles user authentication and project access control.

4.3 Data Flow Diagram (DFD) The system accepts source code as input, processes it through analysis and testing modules, stores results in the database, and displays outputs through the dashboard.

4.4 Use Case Diagram Actors include users and administrators. Use cases include project upload, analysis execution, test execution, and report generation.

4.5 Database Design The database stores user details, project information, analysis results, test results, and historical metrics.

CHAPTER 5: IMPLEMENTATION

5.1 Technology Stack

Frontend: React, HTML, CSS Backend: Flask / Node.js Database: MySQL / MongoDB Testing Tools: Unit testing frameworks 5.2 Code Quality Analysis Implementation Static analysis techniques are used to scan source code and detect quality issues based on predefined rules and metrics.

5.3 Automated Testing Implementation The system automatically identifies test files, executes them in an isolated environment, and records results.

5.4 Dashboard Implementation The dashboard displays real-time metrics using interactive charts and tables.

5.5 Security Implementation Authentication mechanisms and secure data handling practices are implemented to protect user data.

CHAPTER 6: TESTING

6.1 Testing Strategy

Unit testing Integration testing System testing 6.2 Test Cases

Test Case ID Description Expected Result TC01 Code upload Successful upload TC02 Run analysis Issues detected TC03 Execute tests Pass/Fail results

6.3 Test Results All test cases were executed successfully, validating system functionality.

CHAPTER 7: RESULTS AND DISCUSSION

7.1 Results The system successfully analyzed code quality, executed automated tests, and displayed results on the dashboard.

7.2 Discussion The project demonstrates effective integration of code analysis and testing, improving efficiency and accuracy.

CHAPTER 8: ADVANTAGES AND LIMITATIONS

8.1 Advantages

Automated quality assessment Time-saving Improved software reliability Centralized visualization 8.2 Limitations

Limited language support Basic security analysis CHAPTER 9: FUTURE ENHANCEMENTS

AI-based code recommendations CI/CD pipeline integration Multi-language support Cloud deployment CHAPTER 10: CONCLUSION The Code Quality Analyzer and Automated Testing Dashboard successfully automates code quality evaluation and testing. The system improves development efficiency and ensures higher software quality, making it a valuable tool for developers and organizations. It should use the system to create vector representations of rest api endpoints callable by the system and saving images of whole systems after it is analysed - allowing momentous deployments of actions by launching pre-saved image - Such deployments must use 1- Internal AI loaded with operational mmanuals, and proposed workflow examples for each of the repo infering it's capacity and exact specifics allowing context to work as source of truth allowing using any program as operational node with full knowledge presented internal operator agents that get's injected knowledge Preferably to use LSP to infer code quality context as well as dynamically modifyable nodes of analysis programs- like black, isort, runtime,errors,custom-depos, etc.- allowing enhancing it's inferred resolution from context. Creating RAG inference ready image storing it and providing vector features functions for sub-agents to use Spawning Recursive languge models with unlimited context from fully inferred background from all ingested and turned on codebases and context is relevantly selected for smart context provision not initializing knowledge about irrelevant inferred contexts which stream knowledge at all times.