CoSQA: 20,000+ Web Queries for Code Search and Question Answering

Daxin Jiang; Duyu Tang; Junjie Huang; Ke Xu; Linjun Shou; Ming Gong; Ming Zhou; Nan Duan

CoSQA: 20,000+ Web Queries for Code Search and Question Answering

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arxiv 2105.13239 v1 pith:UOG7JHPT submitted 2021-05-27 cs.CL cs.SE

CoSQA: 20,000+ Web Queries for Code Search and Question Answering

Junjie Huang , Duyu Tang , Linjun Shou , Ming Gong , Ke Xu , Daxin Jiang , Ming Zhou , Nan Duan This is my paper

classification cs.CL cs.SE

keywords codecosqatrainingansweringcoclrcodesfurtherintroduce

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Finding codes given natural language query isb eneficial to the productivity of software developers. Future progress towards better semantic matching between query and code requires richer supervised training resources. To remedy this, we introduce the CoSQA dataset.It includes 20,604 labels for pairs of natural language queries and codes, each annotated by at least 3 human annotators. We further introduce a contrastive learning method dubbed CoCLR to enhance query-code matching, which works as a data augmenter to bring more artificially generated training instances. We show that evaluated on CodeXGLUE with the same CodeBERT model, training on CoSQA improves the accuracy of code question answering by 5.1%, and incorporating CoCLR brings a further improvement of 10.5%.

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Fine-tuned decoder-only LLMs achieve up to 40.4% higher MAP than UniXcoder on CoSQA+ for code search, with non-monotonic size scaling and data composition sensitivity.