Please use this identifier to cite or link to this item:
Scopus Web of Science® Altmetric
Type: Conference paper
Title: Automated query reformulation for efficient search based on query logs from stack overflow
Author: Cao, K.
Chen, C.
Baltes, S.
Treude, C.
Chen, X.
Citation: International Conference on Software Engineering, 2021, pp.1273-1285
Publisher: IEEE
Issue Date: 2021
Series/Report no.: International Conference on Software Engineering
ISBN: 9781665402965
ISSN: 1558-1225
Conference Name: IEEE/ACM 43rd International Conference on Software Engineering (ICSE) (22 May 2021 - 30 May 2021 : Madrid, Spain)
Statement of
Kaibo Cao, Chunyang Chen, Sebastian Baltes, Christoph Treude, Xiang Chen
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: Stack overflow; data mining; query reformulation; deep learning; query logs
Description: Proceedings of the 43rd IEEE/ACM International Conference on Software Engineering (ICSE 2021)
Rights: ©2021 IEEE
DOI: 10.1109/ICSE43902.2021.00116
Grant ID:
Published version:
Appears in Collections:Computer Science publications

Files in This Item:
There are no files associated with this item.

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.