Modeling User Concerns in the App Store: A Case Study on the Rise and Fall of Yik Yak

Williams, Grant; Mahmoud, Anas

doi:10.1109/re.2018.00-51

Cited by 25 publications

(11 citation statements)

References 41 publications

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“…To accommodate AI and exploit human intelligence in requirements analysis, Dhinakaran et al [88] proposed an active learning approach to classify requirements into features, bugs, rating and user experience. Recently, Williams et al [87] proposed that automated social mining and domain modeling techniques can be used to analyse mobile app success and failure stories to identify end-users' concerns of domain. Khan et al [90], applied AI techniques to a crowd-generated dataset to cluster relevant features and then draw a semi-automated goal model from the extracted features.…”

Section: A Research Map For Intelligent Crowdrementioning

confidence: 99%

“…Recently, Williams et al [87] proposed that automated social mining and domain modeling techniques can be used to analyse crowd-generated requirements. It can be seen in Table 3, possible AI techniques that can used for CrowdRE analysis, validation and modeling are, information retrieval, sentiment analysis, language patterns, annotation, walkthrough, co-modeling, goal modeling, feature modeling and business process modeling, AI argumentation, CBR, Swarm algorithms and collaborative filtering respectively.…”

Section: A Research Map For Intelligent Crowdrementioning

confidence: 99%

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Crowd Intelligence in Requirements Engineering: Current Status and Future Directions

Khan

Liu

Wen

et al. 2019

Lecture Notes in Computer Science

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Software systems are the joint creative products of multiple stakeholders, including both designers and users, based on their perception, knowledge and personal preferences of the application context. The rapid rise in the use of Internet, mobile and social media applications make it even more possible to provide channels to link a large pool of highly diversified and physically distributed designers and end users, the crowd. Converging the knowledge of designers and end users in requirements engineering process is essential for the success of software systems. In this paper, we report the findings of a survey of the literature on crowd-based requirements engineering research. It helps us understand the current research achievements, the areas of concentration, and how requirements related activities can be enhanced by crowd intelligence. Based on the survey, we propose a general research map and suggest the possible future roles of crowd intelligence in requirements engineering.

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Section: A Research Map For Intelligent Crowdrementioning

confidence: 99%

Section: A Research Map For Intelligent Crowdrementioning

confidence: 99%

Crowd Intelligence in Requirements Engineering: Current Status and Future Directions

Khan

Liu

Wen

et al. 2019

Lecture Notes in Computer Science

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“…For example, studies have analysed the relation between user ratings and the vocabulary and length of their reviews (Hoon et al 2012;Vasa et al 2012). Studies have shown that users discuss diverse topics in reviews (Pagano and Maalej 2013;Shams et al 2020), such as app features, qualities (Williams and Mahmoud 2018;Franzmann et al 2020), requirements or issues (Khalid 2013;Alqahtani and Orji 2019;Kalaichelavan et al 2020;Williams et al 2020). For example, using content analysis, researchers analysed recurring types of issues reported by users (McIlroy et al 2016;Wang et al 2020a;Shams et al 2020), their distribution in reviews as well as as relations between app issue type and other information such as price and rating (Iacob et al 2013b; or between issue type and code quality indicators (Di Sorbo et al 2020).…”

Section: Content Analysismentioning

confidence: 99%

Analysing app reviews for software engineering: a systematic literature review

et al. 2022

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App reviews found in app stores can provide critically valuable information to help software engineers understand user requirements and to design, debug, and evolve software products. Over the last ten years, a vast amount of research has been produced to study what useful information might be found in app reviews, and how to mine and organise such information as efficiently as possible. This paper presents a comprehensive survey of this research, covering 182 papers published between 2012 and 2020. This survey classifies app review analysis not only in terms of mined information and applied data mining techniques but also, and most importantly, in terms of supported software engineering activities. The survey also reports on the quality and results of empirical evaluation of existing techniques and identifies important avenues for further research. This survey can be of interest to researchers and commercial organisations developing app review analysis techniques and to software engineers considering to use app review analysis.

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“…It is essential for app developers to address users' problems as their dissatisfaction may lead to the fall of previously Report: SecurityException in OCFileListAdapter: uid 10410 cannot get user data for accounts of type: nextcloud no successful apps [26], [50]. One way to cope with user sat isfaction is to quickly fix frustrating bugs, which may cause users to switch to a competitor and submit negative reviews.…”

Section: A Detecting Bugs Earliermentioning

confidence: 99%

“…Google Play Store and Apple App Store offer together more than ~4 million apps [46] to users. In this market, it is essential for app vendors to regularly release new versions to fix bugs and introduce new features [33], as unsatisfied users are likely to look for alternatives [8], [50]. User dissatisfaction can quickly lead to the fall of even popular apps [26].…”

Section: Introductionmentioning

confidence: 99%

Automatically Matching Bug Reports With Related App Reviews

Haering

Stanik

Maalej

2021

2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE)

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App stores allow users to give valuable feedback on apps, and developers to find this feedback and use it for the software evolution. However, finding user feedback that matches existing bug reports in issue trackers is challenging as users and developers often use a different language. In this work, we introduce DeepMatcher, an automatic approach using stateof-the-art deep learning methods to match problem reports in app reviews to bug reports in issue trackers. We evaluated DeepMatcher with four open-source apps quantitatively and qualitatively. On average, DeepMatcher achieved a hit ratio of 0.71 and a Mean Average Precision of 0.55. For 91 problem reports, DeepMatcher did not find any matching bug report.When manually analyzing these 91 problem reports and the issue trackers of the studied apps, we found that in 47 cases, users actually described a problem before developers discovered and documented it in the issue tracker. We discuss our findings and different use cases for DeepMatcher.

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Modeling User Concerns in the App Store: A Case Study on the Rise and Fall of Yik Yak

Cited by 25 publications

References 41 publications

Crowd Intelligence in Requirements Engineering: Current Status and Future Directions

Crowd Intelligence in Requirements Engineering: Current Status and Future Directions

Analysing app reviews for software engineering: a systematic literature review

Automatically Matching Bug Reports With Related App Reviews

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