Achieving 80% Code Retrieval Accuracy without Vectors or Embeddings
The article presents a heuristic-based approach called SigMap that can achieve 80% code retrieval accuracy without using machine learning models or embeddings. It focuses on extracting and indexing code signatures instead of the full source code.
Why it matters
This approach demonstrates that a significant portion of code retrieval tasks can be solved using simple heuristics, without the need for complex machine learning models or embeddings.
Key Points
- 1SigMap extracts code signatures (function signatures, class names, etc.) and builds an index to quickly match queries
- 2It uses a combination of heuristics like exact token match, symbol name hit, path token match, and prefix match to score and rank relevant files
- 3SigMap achieves 80% Hit@5 accuracy, 5.8x lift over random baseline, and 98.1% token reduction compared to using full source code
Details
The article argues that the full source code contains a lot of noise (boilerplate, imports, loop bodies) that is not relevant for code retrieval tasks. Instead, the author proposes focusing on the compressed representation of code - the identifiers and signatures. SigMap extracts these signatures using language-specific regex extractors, builds an index, and then scores files at query time using a combination of heuristics. This approach avoids the information loss that comes with embedding the full code into dense vectors. The benchmark results show that SigMap can achieve 80% Hit@5 accuracy, 5.8x lift over random baseline, and 98.1% token reduction compared to using full source code. The article also discusses the limitations of this approach, such as handling implicit intent, synonyms, and multi-hop queries, which may require more advanced techniques like embeddings.
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