Ananya Premchand@1rizzlerr

Modern software projects often have test suites where every test passes, creating a false sense of confidence. However, passing tests do not necessarily mean that the tests adequately cover important behaviors, boundary conditions, or failure scenarios. Developers may not know which parts of their test suite are actually weak or what additional tests would provide meaningful coverage. TestPilot addresses this problem by analyzing the behavioral strength of Python/pytest test suites using mutation testing and AI-assisted analysis. A developer provides an existing Python project and its tests. TestPilot discovers the relevant source files, generates controlled code mutations such as changing >= to >, == to !=, or + to -, and executes the existing test suite against each mutation. If the tests detect the change, the mutation is killed. If all tests still pass, the mutation survives, revealing a potential blind spot in the test suite. TestPilot then analyzes surviving mutations and provides an explanation of the behavioral gap, identifies the missing scenario, assesses the associated risk, and generates a targeted test suggestion. For example, if a discount function uses total >= 100 but the test suite never checks exactly 100, TestPilot can identify that boundary condition and suggest a test specifically targeting it. The target users are software developers, QA engineers, students, and teams who want to understand the quality of their tests rather than simply whether their tests are green. Users interact with TestPilot through a web dashboard that presents mutation results, mutation score, killed and surviving mutations, and AI-generated insights. What makes TestPilot unique is its focus on behavioral test quality rather than test quantity or code coverage alone. It connects mutation testing with AI-generated explanations and actionable test recommendations, turning a technical mutation-testing result into an understandable quality insight. By automatically discovering source files, running mutations, executing tests, classifying results, and analyzing surviving mutations, TestPilot provides an efficient workflow for finding hidden weaknesses in existing test suites.

💻 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