NS3: Neuro-Symbolic Semantic Code Search

• URL: http://arxiv.org/abs/2205.10674v1
• Date: Sat, 21 May 2022 20:55:57 GMT
• Title: NS3: Neuro-Symbolic Semantic Code Search
• Authors: Shushan Arakelyan, Anna Hakhverdyan, Miltiadis Allamanis, Christophe Hauser, Luis Garcia and Xiang Ren
• Abstract summary: We use a Neural Module Network architecture to implement this idea. We compare our model - NS3 (Neuro-Symbolic Semantic Search) - to a number of baselines, including state-of-the-art semantic code retrieval methods. We demonstrate that our approach results in more precise code retrieval, and we study the effectiveness of our modular design when handling compositional queries.
• Score: 33.583344165521645
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Semantic code search is the task of retrieving a code snippet given a textual description of its functionality. Recent work has been focused on using similarity metrics between neural embeddings of text and code. However, current language models are known to struggle with longer, compositional text, and multi-step reasoning. To overcome this limitation, we propose supplementing the query sentence with a layout of its semantic structure. The semantic layout is used to break down the final reasoning decision into a series of lower-level decisions. We use a Neural Module Network architecture to implement this idea. We compare our model - NS3 (Neuro-Symbolic Semantic Search) - to a number of baselines, including state-of-the-art semantic code retrieval methods, and evaluate on two datasets - CodeSearchNet and Code Search and Question Answering. We demonstrate that our approach results in more precise code retrieval, and we study the effectiveness of our modular design when handling compositional queries.

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