How Do We Evaluate Vector-Based Code Retrieval?

Modern coding assistants and agents heavily rely on code retrieval systems, which use embedding models to vectorize queries and code for efficient search within large repositories. Despite their prevalence, a significant challenge remains in evaluating the quality of these systems due to a lack of comprehensive benchmarking datasets featuring diverse and reasoning-intensive queries. Voyage AI has addressed this issue by gathering insights from industry partners and developing internal benchmarking tools to enhance code retrieval evaluation. The company identifies common retrieval subtasks, such as text-to-code, code-to-code, and docstring-to-code, and critiques existing benchmarks like CodeSearchNet and CoSQA for their limitations, including noisy labels and overfitting issues. To create better datasets, Voyage AI proposes repurposing question-answer datasets and leveraging code repositories with issues as queries. Their proprietary evaluation suite, which includes diverse datasets across multiple programming languages, aims to provide a more accurate reflection of real-world retrieval quality. Future plans involve sharing in-house datasets to foster community collaboration and improve benchmarking standards.

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