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Neural Code Search Evaluation Dataset

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arxiv 1908.09804 v6 pith:3KJIIPY7 submitted 2019-08-26 cs.SE

Hongyu Li , Seohyun Kim , Satish Chandra This is my paper · ORCID
classification cs.SE
keywords codedatasetevaluationsearchavailablelanguagemodelsnatural
verification ladder T0 review T1 audit T2 compute T3 formal
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There has been an increase of interest in code search using natural language. Assessing the performance of such code search models can be difficult without a readily available evaluation suite. In this paper, we present an evaluation dataset consisting of natural language query and code snippet pairs, with the hope that future work in this area can use this dataset as a common benchmark. We also provide the results of two code search models ([1] and [6]) from recent work. The evaluation dataset is available at https://github.com/facebookresearch/Neural-Code-Search-Evaluation-Dataset

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  1. Structural Code Search using Natural Language Queries

    cs.SE 2025-07 conditional novelty 6.0 of 10

    An LLM-based system translates English code search questions into Semgrep and GQL queries, reaching 55-70% precision/recall on a new synthetic 400-query Java benchmark.

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