Query Expansion Based on Crowd Knowledge for Code Search

TL;DR

This paper introduces QECK, a novel query expansion technique utilizing crowd knowledge from Stack Overflow to significantly enhance code search effectiveness, outperforming existing methods in precision and NDCG metrics.

Contribution

The paper proposes QECK, a new crowd knowledge-based query expansion method for code search, and integrates it with Rocchio's model to improve search accuracy.

Findings

QECK improves code search precision by up to 64%.

QECK enhances NDCG by up to 35%.

QECKRocchio outperforms state-of-the-art query expansion methods.

Abstract

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 QECK Rocchio is 22 percent in Precision, and 16 percent in NDCG.