💻 Programmierung & Entwicklung

Comprehensive Repository Analysis and Bug Fixing Framework

📁 Programmierung & Entwicklung 👤 Beigetragen von @ravidulundu 🗓️ Aktualisiert 2026-04-19

Der Prompt

Act as a comprehensive repository analysis and bug-fixing expert. You are tasked with conducting a thorough analysis of the entire repository to identify, prioritize, fix, and document ALL verifiable bugs, security vulnerabilities, and critical issues across any programming language, framework, or technology stack. Your task is to: - Perform a systematic and detailed analysis of the repository. - Identify and categorize bugs based on severity, impact, and complexity. - Develop a step-by-step process for fixing bugs and validating fixes. - Document all findings and fixes for future reference. ## Phase 1: Initial Repository Assessment You will: 1. Map the complete project structure (e.g., src/, lib/, tests/, docs/, config/, scripts/). 2. Identify the technology stack and dependencies (e.g., package.json, requirements.txt). 3. Document main entry points, critical paths, and system boundaries. 4. Analyze build configurations and CI/CD pipelines. 5. Review existing documentation (e.g., README, API docs). ## Phase 2: Systematic Bug Discovery You will identify bugs in the following categories: 1. **Critical Bugs:** Security vulnerabilities, data corruption, crashes, etc. 2. **Functional Bugs:** Logic errors, state management issues, incorrect API contracts. 3. **Integration Bugs:** Database query errors, API usage issues, network problems. 4. **Edge Cases:** Null handling, boundary conditions, timeout issues. 5. **Code Quality Issues:** Dead code, deprecated APIs, performance bottlenecks. ### Discovery Methods: - Static code analysis. - Dependency vulnerability scanning. - Code path analysis for untested code. - Configuration validation. ## Phase 3: Bug Documentation & Prioritization For each bug, document: - BUG-ID, Severity, Category, File(s), Component. - Description of current and expected behavior. - Root cause analysis. - Impact assessment (user/system/business). - Reproduction steps and verification methods. - Prioritize bugs based on severity, user impact, and complexity. ## Phase 4: Fix Implementation 1. Create an isolated branch for each fix. 2. Write a failing test first (TDD). 3. Implement minimal fixes and verify tests pass. 4. Run regression tests and update documentation. ## Phase 5: Testing & Validation 1. Provide unit, integration, and regression tests for each fix. 2. Validate fixes using comprehensive test structures. 3. Run static analysis and verify performance benchmarks. ## Phase 6: Documentation & Reporting 1. Update inline code comments and API documentation. 2. Create an executive summary report with findings and fixes. 3. Deliver results in Markdown, JSON/YAML, and CSV formats. ## Phase 7: Continuous Improvement 1. Identify common bug patterns and recommend preventive measures. 2. Propose enhancements to tools, processes, and architecture. 3. Suggest monitoring and logging improvements. ## Constraints: - Never compromise security for simplicity. - Maintain an audit trail of changes. - Follow semantic versioning for API changes. - Document assumptions and respect rate limits. Use variables like ${repositoryName} for repository-specific details. Provide detailed documentation and code examples when necessary.

0 Kopien

🚀 In ChatGPT öffnen ✨ In Claude öffnen 💎 In Gemini öffnen

Beispiel-Ausgaben

So antworten ChatGPT, Claude und Gemini typischerweise auf diesen Prompt:

🚀 ChatGPT

I'll conduct a comprehensive repository analysis following your framework. First, I'll map your project structure and identify all technology dependencies, then systematically scan for bugs across all severity levels using static analysis and vulnerability scanning. For each discovered issue, I'll provide a detailed bug report with ID, severity rating, affected files, root cause analysis, and step-by-step remediation instructions with code examples. Finally, I'll create a prioritized action plan with validation tests to ensure all fixes work correctly.

✨ Claude

I appreciate the thoroughness of this framework, though I should note some important limitations. I can analyze code you share and identify potential issues, but I cannot actually access repositories, run static analysis tools, or execute CI/CD pipelines. What I can do is review code snippets you provide, spot common bug patterns, suggest fixes with explanations, and help you structure a bug documentation system. Would you like to share specific files or code sections for me to analyze?

💎 Gemini

I can help analyze code for bugs following this framework. Please provide the repository contents or specific files you'd like analyzed. I'll examine the code for security vulnerabilities (like SQL injection, XSS), logic errors, dependency issues, and code quality problems. For each finding, I'll document the severity, location, root cause, and provide concrete fix recommendations with code examples. Note that I'll need you to share the actual code files to perform the analysis.

So nutzt du diesen Prompt

Kopiere den Prompt oben oder klicke einen "Öffnen in"-Button um ihn direkt in deiner bevorzugten KI zu starten. Du kannst den Text dann an deinen Anwendungsfall anpassen — z.B. Platzhalter wie [dein Thema] durch echten Kontext ersetzen.

Welches KI-Modell funktioniert am besten

Claude Opus 4 und Sonnet 4.6 performen bei Coding-Aufgaben meist besser als ChatGPT und Gemini — stärkeres Reasoning, besser mit langem Kontext (ganze Dateien, Multi-File-Projekte), und ehrlicher über Unsicherheit. ChatGPT ist schneller für Quick-Snippets; Gemini ist am besten wenn Code mit Screenshots oder visuellem Kontext zu tun hat.

Diesen Prompt anpassen

Tausche die im Prompt erwähnte Sprache (Python, JavaScript, etc.) gegen deinen Stack. Für Debugging oder Code-Review fügst du deinen echten Code direkt nach dem Prompt ein. Bei Generierungs-Aufgaben spezifiziere das Framework (React, Vue, Django, FastAPI) und Einschränkungen (max. Zeilen, keine externen Libraries, muss async sein).

Typische Anwendungsfälle

Production-Code mit strikten Style-Vorgaben schreiben
Pull Requests reviewen und Bugs vor dem Merge finden
Zwischen Sprachen konvertieren (Python → TypeScript z.B.)
Unit-Tests für bestehende Funktionen generieren
Unbekannte Codebases für neue Team-Mitglieder erklären

Variationen

Passe den Tonfall an (lockerer, technischer), ändere das Ausgabeformat (Aufzählungen vs. Absätze) oder füge Einschränkungen hinzu (Wortlimits, Zielgruppe).