Mobile app review analysis for crowdsourcing of software requirements: a mapping study of automated and semi-automated tools
Mobile app reviews are valuable for gaining user feedback on features, usability, and areas for improvement. Analyzing these reviews manually is difficult due to volume and structure, leading to the need for automated techniques. This mapping study categorizes existing approaches for automated and s...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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PeerJ Inc.
2024-11-01
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| Series: | PeerJ Computer Science |
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| Online Access: | https://peerj.com/articles/cs-2401.pdf |
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| _version_ | 1850198922032578560 |
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| author | Rhodes Massenon Ishaya Gambo Roseline Oluwaseun Ogundokun Ezekiel Adebayo Ogundepo Sweta Srivastava Saurabh Agarwal Wooguil Pak |
| author_facet | Rhodes Massenon Ishaya Gambo Roseline Oluwaseun Ogundokun Ezekiel Adebayo Ogundepo Sweta Srivastava Saurabh Agarwal Wooguil Pak |
| author_sort | Rhodes Massenon |
| collection | DOAJ |
| description | Mobile app reviews are valuable for gaining user feedback on features, usability, and areas for improvement. Analyzing these reviews manually is difficult due to volume and structure, leading to the need for automated techniques. This mapping study categorizes existing approaches for automated and semi-automated tools by analyzing 180 primary studies. Techniques include topic modeling, collocation finding, association rule-based, aspect-based sentiment analysis, frequency-based, word vector-based, and hybrid approaches. The study compares various tools for analyzing mobile app reviews based on performance, scalability, and user-friendliness. Tools like KEFE, MERIT, DIVER, SAFER, SIRA, T-FEX, RE-BERT, and AOBTM outperformed baseline tools like IDEA and SAFE in identifying emerging issues and extracting relevant information. The study also discusses limitations such as manual intervention, linguistic complexities, scalability issues, and interpretability challenges in incorporating user feedback. Overall, this mapping study outlines the current state of feature extraction from app reviews, suggesting future research and innovation opportunities for extracting software requirements from mobile app reviews, thereby improving mobile app development. |
| format | Article |
| id | doaj-art-af089087c6494439bbd8482ebf9dc4ae |
| institution | OA Journals |
| issn | 2376-5992 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| spelling | doaj-art-af089087c6494439bbd8482ebf9dc4ae2025-08-20T02:12:45ZengPeerJ Inc.PeerJ Computer Science2376-59922024-11-0110e240110.7717/peerj-cs.2401Mobile app review analysis for crowdsourcing of software requirements: a mapping study of automated and semi-automated toolsRhodes Massenon0Ishaya Gambo1Roseline Oluwaseun Ogundokun2Ezekiel Adebayo Ogundepo3Sweta Srivastava4Saurabh Agarwal5Wooguil Pak6Department of Computer Science and Engineering, Obafemi Awolowo University, Ile-Ife, NigeriaDepartment of Computer Science and Engineering, Obafemi Awolowo University, Ile-Ife, NigeriaDepartment of Multimedia Engineering, Kaunas University of Technology, Kaunas, LithuaniaAfrican Institute for Mathematical Sciences, Kigali, RwandaDepartment of Computer Science & Engineering, Amity University, Noida, IndiaDepartment of Information and Communication Engineering, Yeungnam University, Gyeongsan, Republic of South KoreaDepartment of Information and Communication Engineering, Yeungnam University, Gyeongsan, Republic of South KoreaMobile app reviews are valuable for gaining user feedback on features, usability, and areas for improvement. Analyzing these reviews manually is difficult due to volume and structure, leading to the need for automated techniques. This mapping study categorizes existing approaches for automated and semi-automated tools by analyzing 180 primary studies. Techniques include topic modeling, collocation finding, association rule-based, aspect-based sentiment analysis, frequency-based, word vector-based, and hybrid approaches. The study compares various tools for analyzing mobile app reviews based on performance, scalability, and user-friendliness. Tools like KEFE, MERIT, DIVER, SAFER, SIRA, T-FEX, RE-BERT, and AOBTM outperformed baseline tools like IDEA and SAFE in identifying emerging issues and extracting relevant information. The study also discusses limitations such as manual intervention, linguistic complexities, scalability issues, and interpretability challenges in incorporating user feedback. Overall, this mapping study outlines the current state of feature extraction from app reviews, suggesting future research and innovation opportunities for extracting software requirements from mobile app reviews, thereby improving mobile app development.https://peerj.com/articles/cs-2401.pdfMobile app reviewsCrowdsourcingSoftware requirementsAutomated toolsSemi-automated toolsMapping study |
| spellingShingle | Rhodes Massenon Ishaya Gambo Roseline Oluwaseun Ogundokun Ezekiel Adebayo Ogundepo Sweta Srivastava Saurabh Agarwal Wooguil Pak Mobile app review analysis for crowdsourcing of software requirements: a mapping study of automated and semi-automated tools PeerJ Computer Science Mobile app reviews Crowdsourcing Software requirements Automated tools Semi-automated tools Mapping study |
| title | Mobile app review analysis for crowdsourcing of software requirements: a mapping study of automated and semi-automated tools |
| title_full | Mobile app review analysis for crowdsourcing of software requirements: a mapping study of automated and semi-automated tools |
| title_fullStr | Mobile app review analysis for crowdsourcing of software requirements: a mapping study of automated and semi-automated tools |
| title_full_unstemmed | Mobile app review analysis for crowdsourcing of software requirements: a mapping study of automated and semi-automated tools |
| title_short | Mobile app review analysis for crowdsourcing of software requirements: a mapping study of automated and semi-automated tools |
| title_sort | mobile app review analysis for crowdsourcing of software requirements a mapping study of automated and semi automated tools |
| topic | Mobile app reviews Crowdsourcing Software requirements Automated tools Semi-automated tools Mapping study |
| url | https://peerj.com/articles/cs-2401.pdf |
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