research-article

Caspar: extracting and synthesizing user stories of problems from app reviews

Authors:

Munindar P. SinghAuthors Info & Claims

ICSE '20: Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering

Pages 628 - 640

https://doi.org/10.1145/3377811.3380924

Published: 01 October 2020 Publication History

Abstract

A user's review of an app often describes the user's interactions with the app. These interactions, which we interpret as mini stories, are prominent in reviews with negative ratings. In general, a story in an app review would contain at least two types of events: user actions and associated app behaviors. Being able to identify such stories would enable an app's developer in better maintaining and improving the app's functionality and enhancing user experience.

We present Caspar, a method for extracting and synthesizing user-reported mini stories regarding app problems from reviews. By extending and applying natural language processing techniques, Caspar extracts ordered events from app reviews, classifies them as user actions or app problems, and synthesizes action-problem pairs. Our evaluation shows that Caspar is effective in finding action-problem pairs from reviews. First, Caspar classifies the events with an accuracy of 82.0% on manually labeled data. Second, relative to human evaluators, Caspar extracts event pairs with 92.9% precision and 34.2% recall. In addition, we train an inference model on the extracted action-problem pairs that automatically predicts possible app problems for different use cases. Preliminary evaluation shows that our method yields promising results. Caspar illustrates the potential for a deeper understanding of app reviews and possibly other natural language artifacts arising in software engineering.

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Cited By

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https://doi.org/10.1016/j.infsof.2024.107597
Domínguez FQuintana MCinco RBorrego GGonzález-López S(2024)User Story Automation in Software Engineering: Insights from the Literature2024 12th International Conference in Software Engineering Research and Innovation (CONISOFT)10.1109/CONISOFT63288.2024.00044(279-286)Online publication date: 28-Oct-2024
https://doi.org/10.1109/CONISOFT63288.2024.00044
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cover image ACM Conferences

ICSE '20: Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering

June 2020

1640 pages

ISBN:9781450371216

DOI:10.1145/3377811

General Chairs:
Gregg Rothermel
North Carolina State University
,
Doo-Hwan Bae
KAIST, South Korea

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Published: 01 October 2020

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June 27 - July 19, 2020

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Cited By

Wang YWang JZhang HMing XWang Q(2025)Better together: Automated app review analysis with deep multi-task learningInformation and Software Technology10.1016/j.infsof.2024.107597177(107597)Online publication date: Jan-2025
https://doi.org/10.1016/j.infsof.2024.107597
Domínguez FQuintana MCinco RBorrego GGonzález-López S(2024)User Story Automation in Software Engineering: Insights from the Literature2024 12th International Conference in Software Engineering Research and Innovation (CONISOFT)10.1109/CONISOFT63288.2024.00044(279-286)Online publication date: 28-Oct-2024
https://doi.org/10.1109/CONISOFT63288.2024.00044
Wang XZhang TTan YShang WLi Y(2024)How to effectively mine app reviews concerning software ecosystem? A survey of review characteristicsJournal of Systems and Software10.1016/j.jss.2024.112040213(112040)Online publication date: Jul-2024
https://doi.org/10.1016/j.jss.2024.112040
Chen XHu XHuang YJiang HJi WJiang YJiang YLiu BLiu HLi XLian XMeng GPeng XSun HShi LWang BWang CWang JWang TXuan JXia XYang YYang YZhang LZhou YZhang L(2024)Deep learning-based software engineering: progress, challenges, and opportunitiesScience China Information Sciences10.1007/s11432-023-4127-568:1Online publication date: 24-Dec-2024
https://doi.org/10.1007/s11432-023-4127-5
Zhu WProksch SGerman DGodfrey MLi LMcIntosh S(2024)What is an app store? The software engineering perspectiveEmpirical Software Engineering10.1007/s10664-023-10362-329:1Online publication date: 2-Jan-2024
https://dl.acm.org/doi/10.1007/s10664-023-10362-3
Guo SLin HZhao JLi HChen RLi XJiang H(2023)An Easy Data Augmentation Approach for Application Reviews Event InferenceIEEE Transactions on Software Engineering10.1109/TSE.2023.331398949:10(4751-4772)Online publication date: 20-Sep-2023
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Tan YChen JShang WZhang TFang SLuo XChen ZQi S(2023)STRE: An Automated Approach to Suggesting App Developers When to Stop Reading ReviewsIEEE Transactions on Software Engineering10.1109/TSE.2023.328574349:8(4135-4151)Online publication date: 1-Aug-2023
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Hadi MFard F(2023)Evaluating pre-trained models for user feedback analysis in software engineering: a study on classification of app-reviewsEmpirical Software Engineering10.1007/s10664-023-10314-x28:4Online publication date: 23-May-2023
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Grace RBurnham KNa H(2023)How Much Context Do Users Provide in App Reviews? Implications for Requirements ElicitationInformation for a Better World: Normality, Virtuality, Physicality, Inclusivity10.1007/978-3-031-28032-0_2(16-25)Online publication date: 13-Mar-2023
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Ebrahimi FMahmoud A(2022)Unsupervised Summarization of Privacy Concerns in Mobile Application ReviewsProceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering10.1145/3551349.3561155(1-12)Online publication date: 10-Oct-2022
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