Enriching query semantics for code search with reinforcement learning.

Area of Science:

• Software Engineering
• Information Retrieval
• Artificial Intelligence

Background:

• Accurate code search is crucial for software development.
• Deep learning models often rely on code-description pairs, assuming minimal semantic difference between descriptions and user queries.
• This assumption is flawed, leading to suboptimal performance in real-world code search scenarios.

Purpose of the Study:

• To address the semantic gap between natural language queries and code descriptions in code search.
• To propose a novel model, QueCos, that enhances query semantics for more effective code retrieval.
• To improve the performance of code search systems beyond existing state-of-the-art methods.

Main Methods:

• Developed QueCos, a Query-enriched Code search model.
• Utilized reinforcement learning (RL) to train the model to generate semantically enriched queries.
• Employed code search performance as a reward signal within the RL framework to guide query enrichment.

Main Results:

• Demonstrated that models trained on code-description pairs perform poorly on user queries due to semantic distance.
• QueCos successfully generates semantic enriched queries that capture essential query semantics.
• Experimental results on benchmark datasets show significant performance improvements over state-of-the-art code search models.

Conclusions:

• The semantic gap between code descriptions and user queries is a significant challenge in code search.
• QueCos effectively mitigates this gap by enriching queries using reinforcement learning.
• The proposed approach offers a promising direction for developing more accurate and efficient code search systems.