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debasmi basu draft for gsoc 2025 application

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# AI-Powered API Testing and Tool Integration
## Personal Information
- **Full Name:** Debasmi Basu
- **Email:** [basudebasmi2006@gmail.com](mailto:basudebasmi2006@gmail.com)
- **Phone:** +91 7439640610
- **Discord Handle:** debasmibasu
- **Home Page:** [Portfolio](https://debasmi.github.io/portfolio/portfolio.html)
- **GitHub Profile:** [Debasmi](https://github.com/debasmi)
- **Socials:**
- [LinkedIn](https://www.linkedin.com/in/debasmi-basu-513726288/)
- **Time Zone:** Indian Standard Time
- **Resume:** [Google Drive Link](https://drive.google.com/file/d/1o5JxOwneK-jv2GxnKTrzk__n7UbSKTPt/view?usp=sharing)
## 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.