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Code search is a common practice for developers during softwareimplementation. The challenges of accurate code search mainly lie in theknowledge gap between source code and natural language (i.e., queries). Due tothe limited code-query pairs and large code-description pairs available, theprior studies based on deep learning techniques focus on learning the semanticmatching relation between source code and corresponding description texts forthe task, and hypothesize that the semantic gap between descriptions and userqueries is marginal. In this work, we found that the code search models trainedon code-description pairs may not perform well on user queries, which indicatesthe semantic distance between queries and code descriptions. To mitigate thesemantic distance for more effective code search, we propose QueCos, aQuery-enriched Code search model. QueCos learns to generate semantic enrichedqueries to capture the key semantics of given queries with reinforcementlearning (RL). With RL, the code search performance is considered as a rewardfor producing accurate semantic enriched queries. The enriched queries arefinally employed for code search. Experiments on the benchmark datasets showthat QueCos can significantly outperform the state-of-the-art code searchmodels.

Enriching Query Semantics for Code Search with Reinforcement Learning