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Improving Code Search with Co-Attentive Representation Learning

Authors:

Jianhang Shuai,

Yan LeiAuthors Info & Claims

ICPC '20: Proceedings of the 28th International Conference on Program Comprehension

Pages 196 - 207

https://doi.org/10.1145/3387904.3389269

Published: 12 September 2020 Publication History

Abstract

Searching and reusing existing code from a large-scale codebase, e.g, GitHub, can help developers complete a programming task efficiently. Recently, Gu et al. proposed a deep learning-based model (i.e., DeepCS), which significantly outperformed prior models. The DeepCS embedded codebase and natural language queries into vectors by two LSTM (long and short-term memory) models separately, and returned developers the code with higher similarity to a code search query. However, such embedding method learned two isolated representations for code and query but ignored their internal semantic correlations. As a result, the learned isolated representations of code and query may limit the effectiveness of code search.

To address the aforementioned issue, we propose a co-attentive representation learning model, i.e., Co-Attentive Representation Learning Code Search-CNN (CARLCS-CNN). CARLCS-CNN learns interdependent representations for the embedded code and query with a co-attention mechanism. Generally, such mechanism learns a correlation matrix between embedded code and query, and co-attends their semantic relationship via row/column-wise max-pooling. In this way, the semantic correlation between code and query can directly affect their individual representations. We evaluate the effectiveness of CARLCS-CNN on Gu et al.'s dataset with 10k queries. Experimental results show that the proposed CARLCS-CNN model significantly outperforms DeepCS by 26.72% in terms of MRR (mean reciprocal rank). Additionally, CARLCS-CNN is five times faster than DeepCS in model training and four times in testing.

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Index Terms

Improving Code Search with Co-Attentive Representation Learning
1. Software and its engineering
  1. Software creation and management
    1. Search-based software engineering

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ICPC '20: Proceedings of the 28th International Conference on Program Comprehension

July 2020

481 pages

ISBN:9781450379588

DOI:10.1145/3387904

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ICPC '20: 28th International Conference on Program Comprehension

July 13 - 15, 2020

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Zhang XXiang YLiu ZHu XZhou D(2024)I2R: Intra and inter-modal representation learning for code searchIntelligent Data Analysis10.3233/IDA-23008228:3(807-823)Online publication date: 28-May-2024
https://doi.org/10.3233/IDA-230082
HONG JCHOI EMIZUNO O(2024)A Combined Alignment Model for Code SearchIEICE Transactions on Information and Systems10.1587/transinf.2023MPP0002E107.D:3(257-267)Online publication date: 1-Mar-2024
https://doi.org/10.1587/transinf.2023MPP0002
Zhang FLi MWu HWu T(2024)Intelligent code search aids edge software developmentJournal of Cloud Computing10.1186/s13677-024-00629-513:1Online publication date: 1-Apr-2024
https://doi.org/10.1186/s13677-024-00629-5
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