Bug2Fix AI

Software debugging is often a slow and repetitive process. Developers must read bug reports and stack traces, understand unfamiliar code, identify the root cause, reproduce the failure, create tests, implement a fix, run the test suite, and confirm that the change has not introduced new problems. This process requires significant manual effort and can lead to delays, repeated debugging cycles, and unverified fixes. Bug2Fix AI is an evidence-first AI debugging workflow designed to streamline this process. Developers provide a project repository, bug description, error log or stack trace, and optional supporting documents. Bug2Fix analyzes the repository and supporting context, then coordinates specialized agents for code exploration, error analysis, and test analysis. These investigations can run in parallel before being combined into a root-cause analysis. The workflow then follows a reproduce-first approach. Bug2Fix identifies or creates a regression test, confirms the reported failure, generates the smallest appropriate fix, and runs the relevant tests and broader test suite. A scope check also compares the expected and actual files changed. The system only marks a fix as "Verified with Evidence" when the required tests pass and the change is consistent with the requested issue. The target users are software developers, engineering teams, QA engineers, and teams using AI-assisted development workflows. What makes Bug2Fix different is its focus on evidence rather than code generation alone. Instead of simply suggesting that a bug is fixed, it records the root-cause evidence, reproduction result, code changes, test results, and verification status in an evidence ledger. This creates a transparent trail from the original bug report to the final verified fix. Bug2Fix AI aims to reduce debugging time, manual effort, and rework while making AI-assisted software development more reliable, auditable, and practical.