Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

5-2021

Abstract

As a popular Q&A site for programming, Stack Overflow is a treasure for developers. However, the amount of questions and answers on Stack Overflow make it difficult for developers to efficiently locate the information they are looking for. There are two gaps leading to poor search results: the gap between the user's intention and the textual query, and the semantic gap between the query and the post content. Therefore, developers have to constantly reformulate their queries by correcting misspelled words, adding limitations to certain programming languages or platforms, etc. As query reformulation is tedious for developers, especially for novices, we propose an automated software-specific query reformulation approach based on deep learning. With query logs provided by Stack Overflow, we construct a large-scale query reformulation corpus, including the original queries and corresponding reformulated ones. Our approach trains a Transformer model that can automatically generate candidate reformulated queries when given the user's original query. The evaluation results show that our approach outperforms five state-of-the-art baselines, and achieves a 5.6% to 33.5% boost in terms of ExactMatch and a 4.8% to 14.4% boost in terms of GLEU.

Keywords

Data Mining, Deep Learning, Query Logs, Query Reformulation, Stack Overflow

Discipline

Software Engineering

Research Areas

Software and Cyber-Physical Systems

Publication

Proceedings of the 2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE), Madrid, Spain, May 22-30

First Page

1273

Last Page

1285

ISBN

9780738113197

Identifier

10.1109/ICSE43902.2021.00116

Publisher

IEEE Computer Society

City or Country

Los Alamitos, CA

Additional URL

https://doi.org/10.1109/ICSE43902.2021.00116

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