Listening to the Crowd for the Release Planning of Mobile Apps

Scalabrino, Simone; Bavota, Gabriele; Russo, Barbara; Penta, Massimiliano Di; Oliveto, Rocco

doi:10.1109/tse.2017.2759112

Cited by 65 publications

(55 citation statements)

References 29 publications

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“…Villarroel et al [43] and Scalabrino et al [44] conducted a semi-structured interview with 3 project managers of software companies developing mobile apps in order to evaluate the usefulness of their tool (which extracts and clusters user reviews from the app store into bug report or new feature request). The tool was first demonstrated to the managers, then they were asked about the usefulness of reviews (do you analyse user reviews when planning a new release?…”

Section: Related Workmentioning

confidence: 99%

App Store Effects on Software Engineering Practices

Al-Subaihin

Sarro

Black

et al. 2021

IIEEE Trans. Software Eng.

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In this paper, we study the app store as a phenomenon from the developers' perspective to investigate the extent to which app stores affect software engineering tasks. Through developer interviews and questionnaires, we uncover findings that highlight and quantify the effects of three high-level app store themes: bridging the gap between developers and users, increasing market transparency and affecting mobile release management. Our findings have implications for testing, requirements engineering and mining software repositories research fields. These findings can help guide future research in supporting mobile app developers through a deeper understanding of the app store-developer interaction.

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Section: Related Workmentioning

confidence: 99%

App Store Effects on Software Engineering Practices

Al-Subaihin

Sarro

Black

et al. 2021

IIEEE Trans. Software Eng.

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show abstract

“…Using spaCy, we created the part-of-speech tags for the title, the body, and the phrases of a post. While Chaparro et al also used NLP patterns, we opted for a simple, effective, and pretty consolidated approach to classify text, such as the one successfully used by Villarroel et al (2016) and Scalabrino et al (2017), when classifying app reviews.…”

Section: Experimental Setup Using Machine Learning Algorithmsmentioning

confidence: 99%

What kind of questions do developers ask on Stack Overflow? A comparison of automated approaches to classify posts into question categories

et al. 2019

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On question and answer sites, such as Stack Overflow (SO), developers use tags to label the content of a post and to support developers in question searching and browsing. However, these tags mainly refer to technological aspects instead of the purpose of the question. Tagging questions with their purpose can add a new dimension to the identification of discussed topics in posts on SO. In this paper, we aim at automating the classification of SO question posts into seven question categories. As a first step, we harmonized existing taxonomies of question categories and then, we manually classified 1,000 SO questions according to our new taxonomy. Additionally to the question category, we marked the phrases that indicate a question category for each of the posts. We then use this data set to automate the classification of posts using two approaches. For the first approach, we manually analyzed the phrases to find patterns. Based on regular expressions, we implemented a classifier, for each of the categories, that determines whether a post belongs to a category. These regular expressions are derived by analyzing patterns in the phrases. In the second approach, we use the curated data set to train classification models of supervised machine learning algorithms (Random Forest and Support Vector Machines). For the machine learning algorithms, we experimented with 1,312 different configurations regarding the preprocessing of the text and the representation of the input data. Then, we compared the performance of the regex approach with the performance of the best configuration that uses machine learning algorithms on a validation set of 110 posts. The results show that using the regular expression approach, we can classify posts into the correct question category with an average precision and recall of 0.90, and an MCC of 0.68. Additionally, we applied the regex approach on all questions of SO that deal with Android app development and investigated the co-occurrence of question categories in posts. We found that the categories API USAGE, CONCEPTUAL, and DISCREPANCY are the most frequently assigned question categories and that they also occur together frequently. Our approach can be used to support developers in browsing SO discussions or researchers in building recommender systems based on SO.

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“…Following the general idea of incorporating user feedback into typical development process, Di Sorbo et al [16], [17] and Scalabrino et al [14], [66] proposed SURF and CLAP, two approaches aiming at recommending the most important reviews to take into account while planning a new release of a mobile application. CLAP improves AR-MINER by clustering reviews into specific categories (e.g., reports of security issues) and by learning from the app history (or from similar apps) which reviews should be addressed [66]. SURF proposed a first strategy to automatically summarize user feedback in more structured and recurrent topics [17], [34] (e.g., GUI, app pricing, app content, bugs, etc.).…”

Section: User Feedback Analysis and App Successmentioning

confidence: 99%

User Review-Based Change File Localization for Mobile Applications

Zhou

Chen

et al. 2021

IIEEE Trans. Software Eng.

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In the current mobile app development, novel and emerging DevOps practices (e.g., Continuous Delivery, Integration, and user feedback analysis) and tools are becoming more widespread. For instance, the integration of user feedback (provided in the form of user reviews) in the software release cycle represents a valuable asset for the maintenance and evolution of mobile apps. To fully make use of these assets, it is highly desirable for developers to establish semantic links between the user reviews and the software artefacts to be changed (e.g., source code and documentation), and thus to localize the potential files to change for addressing the user feedback. In this paper, we propose RISING (Review Integration via claSsification, clusterIng, and linkiNG), an automated approach to support the continuous integration of user feedback via classification, clustering, and linking of user reviews. RISING leverages domainspecific constraint information and semi-supervised learning to group user reviews into multiple fine-grained clusters concerning similar users' requests. Then, by combining the textual information from both commit messages and source code, it automatically localizes potential change files to accommodate the users' requests. Our empirical studies demonstrate that the proposed approach outperforms the state-of-the-art baseline work in terms of clustering and localization accuracy, and thus produces more reliable results.

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Listening to the Crowd for the Release Planning of Mobile Apps

Cited by 65 publications

References 29 publications

App Store Effects on Software Engineering Practices

App Store Effects on Software Engineering Practices

What kind of questions do developers ask on Stack Overflow? A comparison of automated approaches to classify posts into question categories

User Review-Based Change File Localization for Mobile Applications

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