Recall Before Rerank: Benchmarking Deep Learning Models for Large-Scale Code-to-Code Retrieval

arXiv:2606.27401v1 Announce Type: cross Abstract: Semantic code search and clone detection are essential for software development, maintenance, and reuse. This paper evaluates the effectiveness, efficiency, and scalability of contemporary deep learning models for first-stage recall in large-scale code-to-code search engines. Benchmarking across multiple programming languages and datasets reveals critical limits in the precision and scalability of these models on Terabyte-scale source-code collections. We present LLM-based code normalisation and query-rewriting schemes that yield significant ga

Source: arXiv cs.LG — read the full report at the original publisher.

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