This project aims to develop AI-assisted tools that can enhance the code-base of open-source projects by generating useful pull requests (PRs). The project involves curation and oversight of students along with mentors to ensure the quality and effectiveness of the generated PRs.
Implement a proof-of-concept (POC) for an AI-assisted tool capable of suggesting docstrings and improvements for existing code-base functions and modules. This tool will suggest docstrings and potential enhancements for existing functions and modules. The generated pull requests (PRs) will undergo a review process by students and tech leads to guarantee both accuracy and practical value before being presented for potential integration into open-source software (OSS) projects.
Implement a POC for an AI-assisted tool that reviews a codebase, analyzes existing test coverage, and generates new tests for uncovered code paths. The resulting PRs should create new tests, which are reviewed by students and tech leads before being suggested for inclusion in the codebase.
Implement a POC for an AI-assisted tool capable of fetching the codebase of an open-source project along with its existing issues and commentaries, and then solve each issue using an LLM with a RAG architecture if necessary. The resulting PRs are reviewed by students and tech leads before being proposed to maintainers.
Develop a mechanism to analyze submitted PRs and ensure they adhere to the guidelines of the repository. For example, the tool can suggest fixes for missing docstrings. This process helps maintain the quality and consistency of contributions to the codebase.