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Abstract

App Stores, such as Google Play or the Apple Store, allow users to provide feedback on apps by posting review comments and giving star ratings. These platforms constitute a useful electronic mean in which application developers and users can productively exchange information about apps. Previous research showed that users feedback contains usage scenarios, bug reports and feature requests, that can help app developers to accomplish software maintenance and evolution tasks. However, in the case of the most popular apps, the large amount of received feedback, its unstructured nature and varying quality can make the identification of useful user feedback a very challenging task. In this paper we present a taxonomy to classify app reviews into categories relevant to software maintenance and evolution, as well as an approach that merges three techniques: (1) Natural Language Processing, (2) Text Analysis and (3) Sentiment Analysis to automatically classify app reviews into the proposed categories. We show that the combined use of these techniques allows to achieve better results (a precision of 75% and a recall of 74%) than results obtained using each technique individually (precision of 70% and a recall of 67%).Posted at the Zurich Open Repository and Archive, University of Zurich ZORA URL: https://doi.org/10.5167/uzh-113425 Accepted Version Originally published at: Panichella, Sebastiano; Di Sorbo, Andrea; Guzman, Emitza; Visaggio, Corrado Aaron; Canfora, Gerardo; Gall, Harald (2015). How can I improve my app? Classifying user reviews for software maintenance and evolution. In: ICSME 2015. IEEE International Conference on Software Maintenance and Evolution, Bremen, 29 September 2015 -1 October 2015.How Can I Improve My App? Classifying User Reviews for Software Maintenance and Evolution S. Panichella * , A. Di Sorbo † , E. Guzman ‡ , C. A.Visaggio † , G. Canfora † and H. C. Gall * * University of Zurich, Switzerland † University of Sannio, Benevento, Italy ‡ Technische Universität München, Garching, Germany panichella@ifi.uzh.ch, disorbo@unisannio.it, emitza.guzman@mytum.de, {visaggio,canfora}@unisannio.it, gall@ifi.uzh.ch Abstract-App Stores, such as Google Play or the Apple Store, allow users to provide feedback on apps by posting review comments and giving star ratings. These platforms constitute a useful electronic mean in which application developers and users can productively exchange information about apps. Previous research showed that users feedback contains usage scenarios, bug reports and feature requests, that can help app developers to accomplish software maintenance and evolution tasks. However, in the case of the most popular apps, the large amount of received feedback, its unstructured nature and varying quality can make the identification of useful user feedback a very challenging task. In this paper we present a taxonomy to classify app reviews into categories relevant to software maintenance and evolution, as well as an approach that merges three techniques: (1) Natural Language Process...

Code-based metrics such as coupling and cohesion are used to measure a system's structural complexity. But dealing with large systems-those consisting of several millions of lines-at the code level faces many problems. An alternative approach is to concentrate on the system's building blocks such as programs or modules as the unit of examination. We present an approach that uses information in a release history of a system to uncover logical dependencies and change patterns among modules. We have developed the approach by working with 20 releases of a large Telecommunications Switching System. We use release information such as version numbers of programs, modules, and subsystems together with change reports to discover common change behavior (i.e. change patterns) of modules. Our approach identifies logical coupling among modules in such a way that potential structural shortcomings can be identified and further examined, pointing to restructuring or reengineering opportunities.