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AI-Powered API Testing and Tool Integration

Personal Information

University Info

  • University Name: Cluster Innovation Centre, University of Delhi
  • Program: B.Tech. in Information Technology and Mathematical Innovations
  • Year: 2023 - Present
  • Expected Graduation Date: 2027

Motivation & Past Experience

Project of Pride: Image Encryption using Quantum Computing Algorithms

This project represents my most significant achievement in the field of quantum computing and cybersecurity. I developed a quantum image encryption algorithm using Qiskit, leveraging quantum superposition and entanglement to enhance security. By implementing the NEQR model, I ensured 100% accuracy in encryption, preventing any data loss. Additionally, I designed advanced quantum circuit techniques to reduce potential decryption vulnerabilities, pushing the boundaries of modern encryption methods.

This project is my pride because it merges cutting-edge quantum computing with practical data security applications, demonstrating the real-world potential of quantum algorithms in cryptography. It reflects my deep technical expertise in Qiskit, Python, and quantum circuits, as well as my passion for exploring future-proof encryption solutions.

Challenges that Motivate Me

I am driven by challenges that push the boundaries of emerging technologies, security, and web development. The intersection of AI, cybersecurity, web applications, and quantum computing excites me because of its potential to redefine secure digital interactions. My passion lies in building robust, AI-powered automation systems that enhance security, efficiency, and accessibility in real-world applications. Additionally, I enjoy working on scalable web solutions, ensuring that modern applications remain secure and user-friendly.

Availability for GSoC

  • Will work full-time on GSoC.
  • I will also dedicate time to exploring LLM-based security frameworks, improving web API integration, and enhancing my expertise in AI-driven automation.

Regular Sync-Ups

  • Yes. I am committed to maintaining regular sync-ups with mentors to ensure steady project progress and discuss improvements in API security and automation.

Interest in API Dash

  • The potential to integrate AI-powered automation for API testing aligns perfectly with my expertise in web development, backend integration, and security automation.
  • I see a great opportunity in enhancing API security validation using AI-driven techniques, ensuring robust schema validation and intelligent error detection.

Areas for Improvement

  • API Dash can expand real-time collaborative testing features, allowing teams to test and debug APIs more efficiently.
  • Enhancing security automation by integrating AI-powered API monitoring would significantly improve API Dash’s effectiveness.

Project Proposal

Title

AI-Powered API Testing and Tool Integration

Abstract

API testing often requires manual test case creation and validation, making it inefficient. Additionally, converting APIs into structured definitions for AI integration is a complex task. This project aims to automate test generation, response validation, and structured API conversion using LLMs and AI agents. The system will provide automated debugging insights and integrate seamlessly with pydantic-ai and langgraph. A benchmarking dataset will also be created to evaluate various LLMs for API testing tasks.

Weekly Timeline

Week Focus Key Deliverables & Achievements
Week 1-2 Research & Architecture Study existing API testing tools, research AI automation methods, explore web-based API testing interfaces, and define the project architecture. Expected Outcome: Clear technical roadmap for implementation.
Week 3-4 API Specification Parsing Develop a parser to extract API endpoints, request methods, authentication requirements, and response formats from OpenAPI specs, Postman collections, and raw API logs. Expected Outcome: Functional API parser capable of structured data extraction and visualization.
Week 5-6 AI-Based Test Case Generation Implement an AI model that analyzes API specifications and generates valid test cases, including edge cases and error scenarios. Expected Outcome: Automated test case generation covering standard, edge, and security cases, integrated into a web-based UI.
Week 7-8 Response Validation & Debugging Develop an AI-powered validation mechanism that checks API responses against expected schemas and detects inconsistencies. Implement logging and debugging tools within a web dashboard to provide insights into API failures. Expected Outcome: AI-driven validation tool with intelligent debugging support.
Week 9-10 Structured API Conversion Design a system that converts APIs into structured tool definitions compatible with pydantic-ai and langgraph, ensuring seamless AI agent integration. Expected Outcome: Automated conversion of API specs into structured tool definitions, with visual representation in a web-based interface.
Week 11-12 Benchmarking & Evaluation Create a dataset and evaluation framework to benchmark different LLMs for API testing performance. Conduct performance testing on generated test cases and validation mechanisms. Expected Outcome: A benchmarking dataset and comparative analysis of LLMs in API testing tasks, integrated into a web-based reporting system.
Final Week Testing & Documentation Perform comprehensive end-to-end testing, finalize documentation, create usage guides, and submit the final project report. Expected Outcome: Fully tested, documented, and ready-to-use AI-powered API testing framework, with a web-based dashboard for interaction and reporting.

Conclusion

This project will significantly enhance API testing automation by leveraging AI-driven test generation, web-based API analysis, and structured tool conversion. The benchmarking dataset will provide a standard evaluation framework for API testing LLMs, ensuring optimal model selection for API validation. The resulting AI-powered API testing framework will improve efficiency, accuracy, security, and scalability, making API Dash a more powerful tool for developers.