Query expansion via learning change sequences

Article type: Research Article

Authors: Zou, Qun^* | Zhang, Changquan

Affiliations: College of Information Engineering, Jiangxi University of Technology, Nanchang, Jiangxi, China

Correspondence: [*] Corresponding author: Qun Zou, College of Information Engineering, Jiangxi University of Technology, Nanchang, Jiangxi, China. %****␣kes-24-kes200033_temp.tex␣Line␣25␣**** E-mail: [email protected].

Abstract: Proksch has proved the changed terms of source code negatively affect code search quality. However, current query expansion (QE) methods always ignore it. In this paper we propose a novel QE method based on the semantics of change sequences (QESC). It not only captures which changes occurred by extracting change sequences from Github commits, but also understands why changes occurred by learning sequence semantics with Deep Belief Network (DBN). Thus it could extract relevant terms to expand or irrelevant terms to exclude from the changes semantically similar to a query. Our experimental results show QESC outperforms the existing QE methods by 15–23% in terms of precision on inspecting the first query result.

Keywords: query expansion, change sequence, DBN

DOI: 10.3233/KES-200033

Journal: International Journal of Knowledge-based and Intelligent Engineering Systems, vol. 24, no. 2, pp. 95-105, 2020