Repoflare

RepoFlare: Eliminating Code Change Blind Spots via Deterministic Graph Intelligence 1. The Problem: High Friction in Code Review and Maintenance When developers modify code during maintenance, feature additions, or onboarding to a new project, understanding downstream impact across files, functions, and tests is high-friction and error-prone. Engineers either grep manually, guess, or rely on slow CI test runs to catch regressions. Conventional AI coding assistants suffer from a critical blind spot: they treat code repositories as unstructured text, re-reading or re-embedding entire codebases on every prompt. This approach is slow, costly, and produces unverifiable assertions without structural proof, leading to review fatigue, rework, and silent production defects. 2. The Solution: RepoFlare Knowledge Graph Engine RepoFlare transforms repository maintenance and code review by decoupling structural impact traversal from semantic reasoning. It builds and incrementally maintains a lightweight, local knowledge graph stored in DuckDB, parsing ASTs via Tree-Sitter to map files, functions, classes, imports, and tests as explicit nodes and edges. When evaluating a change (`git diff`), RepoFlare operates in two distinct phases: 1. Deterministic Impact Traversal (Zero AI Overhead): RepoFlare traverses the graph backwards in milliseconds to identify affected downstream symbols and unit tests. Operating 100% deterministically and offline, it categorizes impact into `DIRECT`, `INDIRECT`, `RELATED`, and `POSSIBLE` dependencies with zero API cost. 2. Grounded AI Explanation: For semantic reasoning, RepoFlare submits only a bounded, graph-selected context package to the LLM. Every claim in the response mandates explicit, line-level citations (e.g., `[utils.py#L1-L3]`), enabling reviewers to verify reasoning immediately rather than blindly trusting LLM output. 3. Prototype Built with IBM Bob 2.0 We leveraged IBM Bob 2.0 in Agent Mode to engineer two production-grade subsystems of our working prototype from architectural specifications: VS Code Extension Shell: Bob autonomously built the entire TypeScript extension layer (`rpc.ts`, `extension.ts`, `panel.ts`, `webview.ts`), implementing a JSON-RPC 2.0 stdio client that communicates seamlessly with our shared Python core, complete with an interactive SVG dependency graph renderer. DuckDB Response Caching Module: Bob engineered `cache/provider.py`, implementing content-hash-keyed result caching in DuckDB. Repeated queries on unchanged code hit the local cache instantly, eliminating redundant network calls and reducing LLM latency to zero. 4. Measurable Impact on Developer Workflows RepoFlare streamlines developer workflows across code review, onboarding, and application maintenance: Reduces Manual Effort & Rework: Converts multi-hour manual dependency tracing into a <10ms deterministic graph lookup, eliminating pre-merge blind spots. Dramatically Cuts API Cost & Latency: Bounded context packages and Bob’s caching module reduce LLM token consumption by over 80% compared to full-repo re-indexing. Zero-Trust Verification: Line-level citations remove ambiguity during code reviews, allowing developers to audit AI explanation claims in seconds. RepoFlare proves that pairing deterministic graph traversal with targeted AI reasoning creates a faster, cheaper, and fully verifiable workflow for modern software development