DebugPilot AI – Autonomous AI Debugging Agent

Problem: Debugging Applications Software developers debug applications a substantial amount, and when an error occurs, they have to manually read error logs, search through file(s), trace the flow of execution, identify the root cause of the error, create a fix, and write/run tests. This is especially time-costly if the project is large or the developer is unfamiliar with the file(s). It can also introduce incorrect fixes or require additional debugging. Solution: DebugPilot AI is an autonomous AI debugging agent that streamlines the entire debugging process. A developer can input an error, stack trace, or log alongside the project code. DebugPilot AI uses multiple specialized AI agents to investigate the problem. One Code Analysis Agent reviews the code, a Log Analysis Agent analyzes the error and logs, and a Dependency Agent investigates the dependencies and configurations. It collates findings to identify potential root causes and supporting evidence. A Fix Agent then proposes a minimal code fix for the developer to review/approve. Once approved, a Test Agent generates a regression test and validates the fix. If the test validates unsuccessfully, the system can debug the failure and attempt another solution. Target Users: DebugPilot AI targets software developers, development teams, students, and organizations working with large or unfamiliar codebases. How to Use: A user would initiate the DebugPilot AI by selecting a project/debugging task and inputting an error, stack trace, or log. They then can observe as the system tracks the AI agents investigating the error, review the root cause, evidence, and proposed fix, approve the fix, and view the test and validation result. The system then collates everything into a debugging report. Creativity & Uniqueness: DebugPilot AI is more than an AI coding chatbot. Instead, it is an agentic system that utilizes multiple agents that work concurrently to debug applications. It takes the investigative work, root-cause analysis and debugs, creates a minimal viable fix, and finally, tests it using regression testing and validation. The solution creatively uses IBM Bob 2.0's agentic capabilities, parallel tasks, and subagents to identify and resolve a stack trace/error in a real-world application development setting. It improves upon a traditional Error → Investigation → Root Cause → Fix → Test → Pass workflow, utilizing an autonomous fix and test instead of manual human effort.