Code2LoRA: Repository Context without Overhead

Large language models for code grapple with the critical need for repository-level context, understanding imports, APIs, and project conventions. Traditional approaches, relying on extensive retrieval-augmented generation (RAG) or per-repository fine-tuning, impose significant computational costs and struggle with dynamic codebases. This limitation is now addressed by Code2LoRA, a novel hypernetwork framework.

Injecting Repository Knowledge with Zero Inference Overhead

Code2LoRA generates repository-specific LoRA adapters on the fly. This ingenious method injects crucial repository knowledge without increasing inference-time token consumption, a substantial departure from prior methods that either bloat input sequences or require costly fine-tuning. The framework offers two distinct modes: Code2LoRA-Static is optimized for static code snapshots, ideal for analyzing stable projects. Code2LoRA-Evo, on the other hand, dynamically maintains and updates adapters via a GRU hidden state, directly responding to code differences (diffs) and thus perfectly suited for actively developing projects.

RepoPeftBench: A New Standard for Evaluating Parameter-Efficient Code Adaptation

To rigorously assess Code2LoRA's efficacy, the researchers introduced RepoPeftBench. This comprehensive benchmark comprises 604 Python repositories, featuring a static track with 40,000 training and 12,000 test assertion-completion tasks, and an evolution track with 215,000 commit-derived training and 87,000 commit-derived test tasks. On the static track, Code2LoRA-Static demonstrated impressive results, achieving 63.8% cross-repo and 66.2% in-repo exact match, effectively matching the performance ceiling of per-repository LoRA. The evolution track saw Code2LoRA-Evo achieve 60.3% cross-repo exact match, a significant 5.2 percentage point improvement over a single, shared LoRA model.