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论文类型：期刊论文

发表时间：2016-09-01

发表刊物：IEEE TRANSACTIONS ON SERVICES COMPUTING

收录刊物：SCIE、EI、Scopus

卷号：9

期号：5

页面范围：771-783

ISSN号：1939-1374

关键字：Code search; crowd knowledge; query expansion; information retrieval; question & answer pair

摘要：As code search is a frequent developer activity in software development practices, improving the performance of code search is a critical task. In the text retrieval based search techniques employed in the code search, the term mismatch problem is a critical language issue for retrieval effectiveness. By reformulating the queries, query expansion provides effective ways to solve the term mismatch problem. In this paper, we propose Query Expansion based on Crowd Knowledge (QECK), a novel technique to improve the performance of code search algorithms. QECK identifies software-specific expansion words from the high quality pseudo relevance feedback question and answer pairs on Stack Overflow to automatically generate the expansion queries. Furthermore, we incorporate QECK in the classic Rocchio's model, and propose QECK based code search method QECKRocchio. We conduct three experiments to evaluate our QECK technique and investigate QECKRocchio in a large-scale corpus containing real-world code snippets and a question and answer pair collection. The results show that QECK improves the performance of three code search algorithms by up to 64 percent in Precision, and 35 percent in NDCG. Meanwhile, compared with the state-of-the-art query expansion method, the improvement of QECKRocchio is 22 percent in Precision, and 16 percent in NDCG.