research-article

AnswerBot: an answer summary generation tool based on stack overflow

Authors:

Xin Xia,

Zhenchang XingAuthors Info & Claims

ESEC/FSE 2019: Proceedings of the 2019 27th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering

Pages 1134 - 1138

https://doi.org/10.1145/3338906.3341186

Published: 12 August 2019 Publication History

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Abstract

Software Q&A sites (like Stack Overflow) play an essential role in developers’ day-to-day work for problem-solving. Although search engines (like Google) are widely used to obtain a list of relevant posts for technical problems, we observed that the redundant relevant posts and sheer amount of information barriers developers to digest and identify the useful answers. In this paper, we propose a tool AnswerBot which enables to automatically generate an answer summary for a technical problem. AnswerBot consists of three main stages, (1) relevant question retrieval, (2) useful answer paragraph selection, (3) diverse answer summary generation. We implement it in the form of a search engine website. To evaluate AnswerBot, we first build a repository includes a large number of Java questions and their corresponding answers from Stack Overflow. Then, we conduct a user study that evaluates the answer summary generated by AnswerBot and two baselines (based on Google and Stack Overflow search engine) for 100 queries. The results show that the answer summaries generated by AnswerBot are more relevant, useful, and diverse. Moreover, we also substantially improved the efficiency of AnswerBot (from 309 to 8 seconds per query).

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Cited By

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Li MMa YLi YBai Y(2024)Multimodal representative answer extraction in community question answeringJournal of King Saud University - Computer and Information Sciences10.1016/j.jksuci.2023.10178035:9Online publication date: 1-Feb-2024
https://dl.acm.org/doi/10.1016/j.jksuci.2023.101780
Du XLou YLiu MPeng XYang TChandra SBlincoe KTonella P(2023)KG4CraSolver: Recommending Crash Solutions via Knowledge GraphProceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering10.1145/3611643.3616317(1242-1254)Online publication date: 30-Nov-2023
https://dl.acm.org/doi/10.1145/3611643.3616317
Khairunnesa SAhmed SImtiaz SRajan HLeavens G(2023)What kinds of contracts do ML APIs need?Empirical Software Engineering10.1007/s10664-023-10320-z28:6Online publication date: 17-Oct-2023
https://dl.acm.org/doi/10.1007/s10664-023-10320-z
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Index Terms

AnswerBot: an answer summary generation tool based on stack overflow
1. Software and its engineering
  1. Software creation and management
    1. Software development techniques

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Published In

ESEC/FSE 2019: Proceedings of the 2019 27th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering

August 2019

1264 pages

ISBN:9781450355728

DOI:10.1145/3338906

General Chairs:
Marlon Dumas
University of Tartu, Estonia
,
Dietmar Pfahl
University of Tartu, Estonia
,
Program Chairs:
Sven Apel
Saarland University, Germany
,
Alessandra Russo
Imperial College, UK

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 August 2019

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ESEC/FSE '19

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SIGSOFT

ESEC/FSE '19: 27th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering

August 26 - 30, 2019

Tallinn, Estonia

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Overall Acceptance Rate 112 of 543 submissions, 21%

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Li MMa YLi YBai Y(2024)Multimodal representative answer extraction in community question answeringJournal of King Saud University - Computer and Information Sciences10.1016/j.jksuci.2023.10178035:9Online publication date: 1-Feb-2024
https://dl.acm.org/doi/10.1016/j.jksuci.2023.101780
Du XLou YLiu MPeng XYang TChandra SBlincoe KTonella P(2023)KG4CraSolver: Recommending Crash Solutions via Knowledge GraphProceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering10.1145/3611643.3616317(1242-1254)Online publication date: 30-Nov-2023
https://dl.acm.org/doi/10.1145/3611643.3616317
Khairunnesa SAhmed SImtiaz SRajan HLeavens G(2023)What kinds of contracts do ML APIs need?Empirical Software Engineering10.1007/s10664-023-10320-z28:6Online publication date: 17-Oct-2023
https://dl.acm.org/doi/10.1007/s10664-023-10320-z
Liu BZhang PShu YGuan ZLu TGu HGu N(2022)Building a Personalized Model for Social Media Textual Content CensorshipProceedings of the ACM on Human-Computer Interaction10.1145/35556576:CSCW2(1-31)Online publication date: 11-Nov-2022
https://dl.acm.org/doi/10.1145/3555657
Izadi MAhmadabadi MSorbo APanichella S(2022)On the evaluation of NLP-based models for software engineeringProceedings of the 1st International Workshop on Natural Language-based Software Engineering10.1145/3528588.3528665(48-50)Online publication date: 21-May-2022
https://dl.acm.org/doi/10.1145/3528588.3528665
Zhu WZhang HHassan AGodfrey M(2022)An empirical study of question discussions on Stack OverflowEmpirical Software Engineering10.1007/s10664-022-10180-z27:6Online publication date: 1-Nov-2022
https://dl.acm.org/doi/10.1007/s10664-022-10180-z
Wang HXia XLo DGrundy JWang X(2021)Automatic Solution Summarization for Crash BugsProceedings of the 43rd International Conference on Software Engineering10.1109/ICSE43902.2021.00117(1286-1297)Online publication date: 22-May-2021
https://dl.acm.org/doi/10.1109/ICSE43902.2021.00117
Melo GAlencar PCowan DJuristo N(2021)A cognitive and machine learning-based software development paradigm supported by contextProceedings of the 43rd International Conference on Software Engineering: New Ideas and Emerging Results10.1109/ICSE-NIER52604.2021.00011(11-15)Online publication date: 25-May-2021
https://dl.acm.org/doi/10.1109/ICSE-NIER52604.2021.00011
Romero RParra EHaiduc S(2020)Experiences Building an Answer Bot for GitterProceedings of the IEEE/ACM 42nd International Conference on Software Engineering Workshops10.1145/3387940.3391505(66-70)Online publication date: 27-Jun-2020
https://dl.acm.org/doi/10.1145/3387940.3391505
Wessel MSteinmacher I(2020)The Inconvenient Side of Software Bots on Pull RequestsProceedings of the IEEE/ACM 42nd International Conference on Software Engineering Workshops10.1145/3387940.3391504(51-55)Online publication date: 27-Jun-2020
https://dl.acm.org/doi/10.1145/3387940.3391504
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