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Deep learning the semantics of change sequences for query expansion
Author(s) -
Huang Qing,
Yang Yang,
Cheng Ming
Publication year - 2019
Publication title -
software: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.437
H-Index - 70
eISSN - 1097-024X
pISSN - 0038-0644
DOI - 10.1002/spe.2736
Subject(s) - query expansion , computer science , query optimization , sargable , code (set theory) , web search query , semantics (computer science) , web query classification , query language , quality (philosophy) , information retrieval , source code , artificial intelligence , data mining , search engine , programming language , philosophy , set (abstract data type) , epistemology
Summary The overexpansion problem negatively affects the quality of query expansion. To improve the quality of queries for searching code, this paper proposed a DBN‐based algorithm for effective query expansion. The deep belief network (DBN) model is trained on the code sequences and their change sequences, which aims to capture the meaningful terms during the evolution of source code. In contrast to previous studies, the proposed model not only extracts relevant terms to expand a query but also excludes irrelevant terms from the query. It addresses two problems in query expansion, including the overexpansion of the original query and the negative influence of the changed terms in the target source code. Experiments on both artificial queries and real queries show that the proposed algorithm outperforms several query expansion algorithms for code search.