# Neural Code Search Evaluation Dataset

arXiv: https://arxiv.org/abs/1908.09804 · HTML: https://mlsanity.com/paper/1908.09804/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Aug 2019.

## Built on by (most cited first, 13 of 13)

- [OctoPack: Instruction Tuning Code Large Language Models](https://mlsanity.com/paper/2308.07124/) (2023)
- [CoSQA: 20,000+ Web Queries for Code Search and Question Answering](https://mlsanity.com/paper/2105.13239/) (2021)
- [Unifying the Perspectives of NLP and Software Engineering: A Survey on Language Models for Code](https://mlsanity.com/paper/2311.07989/) (2023)
- [On the Importance of Building High-quality Training Datasets for Neural Code Search](https://mlsanity.com/paper/2202.06649/) (2022)
- [CodeRetriever: Unimodal and Bimodal Contrastive Learning for Code Search](https://mlsanity.com/paper/2201.10866/) (2022)
- [GraphSearchNet: Enhancing GNNs via Capturing Global Dependencies for Semantic Code Search](https://mlsanity.com/paper/2111.02671/) (2021)
- [FreshStack: Building Realistic Benchmarks for Evaluating Retrieval on Technical Documents](https://mlsanity.com/paper/2504.13128/) (2025)
- [Can Machines Read Coding Manuals Yet? -- A Benchmark for Building Better Language Models for Code Understanding](https://mlsanity.com/paper/2109.07452/) (2021)
- [A Survey of Deep Learning Models for Structural Code Understanding](https://mlsanity.com/paper/2205.01293/) (2022)
- [Senatus -- A Fast and Accurate Code-to-Code Recommendation Engine](https://mlsanity.com/paper/2111.04473/) (2021)
- [A Survey on Artificial Intelligence for Source Code: A Dialogue Systems Perspective](https://mlsanity.com/paper/2202.04847/) (2022)
- [AstBERT: Enabling Language Model for Financial Code Understanding with Abstract Syntax Trees](https://mlsanity.com/paper/2201.07984/) (2022)
- [Searching by Code: a New SearchBySnippet Dataset and SnippeR Retrieval Model for Searching by Code Snippets](https://mlsanity.com/paper/2305.11625/) (2023)

## Abstract (arXiv)

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