BUGPROOF - From Bug Report to Verified Fix

💡 Inspiration Debugging is one of the most time-consuming and fragmented parts of software development. Developers must manually navigate unfamiliar repositories, reproduce failures, trace execution paths, formulate hypotheses, draft fixes, and ensure zero regressions.

While generative AI assistants offer quick diff suggestions, they often treat ungrounded hypotheses as facts—introducing subtle bugs or breaking existing tests. We built BugProof to bridge the gap between AI generation and ground-truth verification, replacing guesswork with an evidence-driven debugging pipeline.

⚙️ What it does BugProof is an AI-assisted debugging and verification platform that takes a bug report or failing test and produces an approved, fully verified fix.

The core philosophy behind BugProof is simple: never treat an AI suggestion as a fact. The platform strictly separates model hypotheses from what can be definitively proven through source-code inspection, command execution, patch validation, and automated testing.

🏗️ How it works BugProof consolidates the developer debugging lifecycle into a structured, step-by-step workflow:

Investigate & Trace: The developer submits an issue, bug report, or failing test. BugProof scans the target repository, mapping relevant files, function references, dependencies, and existing test suites.

Evidence Collection & Root Cause Analysis: The engine collects self-verified runtime and static evidence directly from the codebase. It analyzes execution paths to isolate the exact root cause, validating findings against real code rather than assumptions.

Focused Fix Proposal (Human-in-the-Loop): BugProof generates a targeted fix proposal alongside an explanation and affected diffs. Fixes are never applied automatically—the developer retains full oversight and must explicitly approve the proposal.

Validation & Verified Execution: Once approved, BugProof applies the patch safely in the environment, runs reproduction tests and regression suites, and compiles the final verification results into an auditable evidence report.

🚀 Key Advantages Strict Human-in-the-Loop Control: Code is never modified or pushed without direct developer consent.

Separation of Claims and Proof: Hypotheses generated by the LLM are validated against command outputs, test results, and AST inspections before being asserted as root causes.

Comprehensive Audit Trail: Developers and reviewers receive a clear record of the investigation: original report, gathered evidence, root cause breakdown, proposed diff, approval log, and post-patch test runs.

💻 Tech Stack Ecosystem & Hackathon: Built for the IBM Bob 2.0 Hackathon on Lablab.ai

Categories: Developer Tools, Coding Assistants, Automated Code Generation & Testing

AI & Verification: Integrated LLM orchestration and command-line execution sandboxes for deterministic test validation

Built With

• assistant
• code
• coding
• developer-tools