Multi-intent Description of Keyword Expansion for Code Search

To address the issue of discrepancies between online query data and offline training data in code search research, we propose a novel code search model called multi intent description keyword extension-based code search (MDKE-CS). Our model utilizes offline training data to expand query data, thereby mitigating the impact of insufficient query data and intention differences between training and query data on search results. Furthermore, we construct a multi-intention description keyword vocabulary library based on developers, searchers, and discussants from the StackOverflow Q&A library to further expand the query. To evaluate the effectiveness of MDKE-CS in code search tasks, we conducted comparative experimental analyses using two baseline models, DeepCS and UNIF, as well as WordNet and BM25 extension methods. Our experimental results demonstrate that MDKE-CS outperforms the baseline models in terms of R@1, R@5, R@10, and MRR values.