Muhammad Usaid@Muhammad_Usaid

Reviewing pull requests in large software repositories can be difficult because a change may affect code, tests, dependencies, documentation, and other parts of the repository. Traditional automated reviewers often analyze only the changed lines and can miss important context or produce generic suggestions. Our solution is an agentic AI powered pull request quality checker that analyzes a pull request together with the relevant repository context. It first understands the type and scope of the change, then identifies related files, dependencies, tests, and documentation that may be affected. Specialized analysis steps can then focus on code quality, potential bugs, security concerns, test coverage, and regression risks. Instead of relying only on an AI generated opinion, the system can use relevant tests and other available evidence to support its findings. This allows the final review to distinguish between issues identified through reasoning and issues supported by actual verification. The system is designed for real world repositories, including large and private codebases where sending the entire repository to an AI model would be inefficient or inappropriate. It retrieves relevant context based on the actual pull request and applies analysis according to the type of change. The target users are software developers, open source maintainers, and development teams who want faster and more context aware pull request reviews. The system aims to reduce manual review effort while giving developers clear findings, explanations, and evidence that they can act on.

💻 Online Hackathon | ⏳ Build: 48 hours | 📅 September 25–27, 2026 The official IBM Bob 2.0 hackathon. Build what's next in AI-assisted development, alongside the developers and experts shaping it. ⭐ Get help from experts throughout the event 🤝 Join a team or build solo 💰 Prize pool: $12,000 🧑🏻‍💻 Register before the Kick-Off Stream to start building with Bob