Improving Code Search with Co-Attentive Representation Learning

著者: Jianhang Shuai, Ling Xu, Chao Liu, Meng Yan, Xin Xia, Yan Lei - Proceedings of the 28th International Conference on Program Comprehension 2020 被引用: 124

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.