Keploy

Mentors: Student: Sparshh

Keploy - OSS Code Indexer for Efficient Retrieval. (Project-2) Problem solved - Current AI models struggle to generate high-quality Unit Tests for large codebases without a deep, structured understanding of the source code. Solution Plan- 1. I implemented code parsing using tree-sitter to create semantic chunks, preserving essential context. 2. Then generating embeddings for these chunks with Gemini, openAI, huggigface and store them in a Milvus vector database. 3. I'm utilizing HNSW indexing within Milvus for efficient semantic similarity search. 4. Then I have extracted code relationships (function calls, class inheritance etc.) and store them as a knowledge graph in Neo4j. 5. Embeddings are generated with Gemini :- https://huggingface.co/spaces/mteb/leaderboard 6. I have developed a dynamic indexing mechanism (using file watching/Git hooks, potentially enhanced by Difftastic) to keep both Milvus and Neo4j synchronized with code changes. 7. Then I build a hybrid retrieval system querying both Milvus (for semantic relevance) and Neo4j (for structural context). 8. Then I integrated this combined context into Keploy's existing Unit Test Generation (UTG) workflow, feeding it to the Gemini API for test creation. 9. Then I need further suggestion to implement an LLM-as-a-judge component to evaluate the generated tests. Deliverables:- A robust GraphRAG system integrated into Keploy. This includes codebase indexing (vector embeddings in Milvus, structural graph in Neo4j), dynamic index updates, hybrid context retrieval, and integration with the Gemini, leading to substantially improved Unit Test Generation quality and relevance for users with large or complex codebases. Thanks. Regards Sparsh.

Mentors: Student: Ahmed Mamdouh

Keploy currently needs an automated way to detect idempotency violations and handle noisy fields in test cases. This leads to inconsistencies in test results and potential flaky tests, making it difficult for users to validate API behavior reliably. The solution introduces an idempotency request replayer that verifies if an API request produces consistent responses across multiple replays. It also implements noise detection and configuration mechanisms to identify and manage dynamic fields that can cause flaky tests.

Mentors: Student: Swapnendu Banerjee

Building upon a comprehensive Proof of Concept (POC) [https://poc-frontend-beta.vercel.app/] developed before the GSoC application, this proposal aims to transform the existing prototype into a robust, open-source dashboard solution for Keploy. The project will leverage the foundation of the POC repositories (Backend: github.com/Swapnendu003/poc-backend, Frontend: github.com/Swapnendu003/poc-frontend) to create a comprehensive platform that transforms complex software development metrics into intuitive, customizable visualizations. By developing a modular, real-time dashboard with advanced widget systems, intelligent data processing, and flexible template configurations, the project will empower development teams to gain unprecedented insights into their code merge and test activities. Utilizing Next.js, Go, and modern web technologies, the dashboard will provide a scalable, extensible tool that adapts to diverse project needs, offering real-time updates, in-depth analytics, and a user-friendly interface for tracking and analyzing software development performance.

Mentors: Student: Harsh Vashishtha Sharma

The project tackles inefficiencies in API contract testing for microservices, focusing on schema management, validation, and consistency across distributed systems, which often cause bugs and delays due to fragmented schemas and poor versioning. I propose enhancing Keploy’s contract testing by implementing local schema storage as a mock registry, unifying schemas into a single service contract, adding advanced comparison with dependency resolution and real-time validation, and introducing provider-driven testing with contract publishing and rollback capabilities. My solution builds on a Go-based prototype, utilizing tools like Cobra, YAML, and Docker, with extensibility to S3. Deliverables include an integrated CLI with versioning and multi-format support, schema merging with diffing and real-time validation, and a provider-driven testing framework with rollback, all supported by comprehensive documentation and tests. This will streamline microservice interactions, ensuring scalability and consistency for developers.

Mentors: Student: Gaurav ( euclid )

The project aims to develop an intelligent code review agent that integrates with GitHub and other version control systems (VCS). This tool will provide automated, insightful feedback on code quality by combining traditional static analysis with AI-powered insights. Problem Being Solved Code reviews are often time-consuming and inconsistent, especially when identifying complex issues such as security vulnerabilities, performance bottlenecks, and maintainability concerns. This project addresses these challenges by automating the review process while providing actionable, contextual feedback. Proposed Solution The solution employs a modular webhook-based architecture with the following components: 1. A webhook receiver to capture GitHub events (e.g., pull request creations/updates). 2. A code fetcher to retrieve and parse changed files. 3. An orchestrator to coordinate multiple analysis tools. 4. A result aggregator to combine findings and remove duplicates. 5. A response handler to post intelligent comments directly on pull requests. Key Deliverables 1. A system that provides actionable, contextual feedback rather than merely flagging issues. 2. Support for multiple programming languages (initially Go, JavaScript, and Python). 3. Implementation of dependency vulnerability detection. 4. An AI-powered layer to enhance basic linter outputs. 5. Extensibility for future language support and additional features.