DevPilot AI: From Issue to Verified Fix

Software engineers lose significant time moving between GitHub issues, unfamiliar files, code search, security findings, test generation, and code review. Existing AI coding assistants can generate useful answers and code, but developers still need to determine which repository evidence supports the answer and whether a proposed change is actually testable. DevPilot AI addresses this gap with a repository-aware engineering workflow: Issue → Investigation → Evidence → Root Cause → Plan → Patch → Regression Test → Verification DevPilot is designed for software developers, engineering teams, repository maintainers, and students working with unfamiliar or evolving codebases. Users can start from a repository or issue, inspect relevant files and symbols, ask repository-aware questions, review security findings, generate tests and documentation, create implementation plans, and review proposed engineering actions. The product is presented as a unified engineering workspace containing Repository Intelligence, AI Chat, Code Analysis, Security, Testing, Documentation, Planner, and PR Review. The prototype also includes Demo Mode, allowing the workflow to be evaluated without external model credentials. Our key innovation is treating AI-assisted coding as a closed-loop engineering process, rather than a single chat interaction. DevPilot connects repository retrieval and reasoning to concrete engineering artifacts such as implementation plans, proposed changes, regression tests, and verification results while keeping the human engineer in control. The prototype uses lexical and symbol-aware repository retrieval, repository file/line citations, controlled verification for the bundled demo repository, and automated regression coverage. Current prototype validation achieved: 100% expected-file hit rate across 10 demo retrieval cases 100% expected-symbol hit rate across 5 labeled cases 100% citation-line validity 13 backend + 2 frontend tests passed These metrics specifically evaluate retrieval, citation validity, and prototype regression coverage; they are not claims of overall AI answer correctness or production reliability. The roadmap includes authenticated multi-user workspaces, persistent storage, isolated code-execution sandboxes, GitHub OAuth, branch/PR automation, semantic retrieval, and broader end-to-end evaluation.