Tracefix

TRACEFIX addresses a common but expensive developer workflow: debugging software when the cause of a bug is unclear or difficult to reproduce. When developers receive a bug report, they typically have to manually navigate a repository, understand unfamiliar code, inspect Git history, search for related tests, reproduce the issue, determine the root cause, implement a fix, and then verify that the change actually solved the problem without introducing regressions. Existing AI coding tools can suggest fixes quickly, but a suggested patch does not necessarily prove that the reported bug was reproduced, that the identified code caused it, or that the fix actually works. TRACEFIX turns debugging into an evidence-driven investigation. A developer connects their GitHub account, selects a repository and branch, and submits a bug description in natural language. TRACEFIX then creates an isolated Docker workspace and clones the repository without modifying the developer's original codebase. It detects the project structure and testing framework and runs baseline tests. The investigation is orchestrated using IBM Bob's agentic capabilities. Specialized AI tasks analyze the repository, relevant code paths, Git history, existing tests, and the reported behavior. Instead of immediately changing code, TRACEFIX builds a root-cause hypothesis and attempts to reproduce the issue inside the isolated environment. Once the issue is reproduced, TRACEFIX generates a regression test representing the failure and verifies that the test fails before the fix. The system then generates a targeted patch, applies it inside the isolated workspace, reruns the regression test, and executes the existing test suite. The final investigation report presents the reproduction result, root-cause evidence, regression test, code changes, and verification results. The target users are software developers, engineering teams, QA engineers, and organizations maintaining complex codebases. Developers can interact with TRACEFIX through the web application and, as an integration path, the same engine can be triggered from GitHub Issues or Pull Requests through a GitHub Action. What makes TRACEFIX unique is its focus on verification rather than generation. It does not simply ask, “What code should I change?” It asks, “Can we reproduce the problem, prove the cause, and verify the fix?” Its isolated execution architecture also allows investigations to move from local Docker environments to cloud-based runners, enabling multiple investigations to run in parallel as usage grows.