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...

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Written development communication (e.g. mailing lists, issue trackers) constitutes a precious source of information to build recommenders for software engineers, for example aimed at suggesting experts, or at redocumenting existing source code. In this paper we propose a novel, semi-supervised approach named DECA (Development Emails Content Analyzer) that uses Natural Language Parsing to classify the content of development emails according to their purpose (e.g. feature request, opinion asking, problem discovery, solution proposal, information giving etc), identifying email elements that can be used for specific tasks. A study based on data from Qt and Ubuntu, highlights a high precision (90%) and recall (70%) of DECA in classifying email content, outperforming traditional machine learning strategies. Moreover, we successfully used DECA for re-documenting source code of Eclipse and Lucene, improving the recall, while keeping high precision, of a previous approach based on ad-hoc heuristics.

Google Play, Apple App Store and Windows Phone Store are well known distribution platforms where users can download mobile apps, rate them and write review comments about the apps they are using. Previous research studies demonstrated that these reviews contain important information to help developers improve their apps. However, analyzing reviews is challenging due to the large amount of reviews posted every day, the unstructured nature of reviews and its varying quality. In this demo we present ARdoc, a tool which combines three techniques: (1) Natural Language Parsing (NLP), (2) Text Analysis (TA) and (3) Sentiment Analysis (SA) to automatically classify useful feedback contained in app reviews important for performing software maintenance and evolution tasks. Our quantitative and qualitative analysis (involving mobile professional developers) demonstrate that ARdoc correctly classies feedback useful for maintenance perspectives in user reviews with high precision (ranging between 84% and 89%), recall (ranging between 84% and 89%), and an F-Measure (ranging between 84% and 89%). While evaluating our tool we also found that ARdoc substantially helps to extract important maintenance tasks for real world applications. ABSTRACTGoogle Play, Apple App Store and Windows Phone Store are well known distribution platforms where users can download mobile apps, rate them and write review comments about the apps they are using. Previous research studies demonstrated that these reviews contain important information to help developers improve their apps. However, analyzing reviews is challenging due to the large amount of reviews posted every day, the unstructured nature of reviews and its varying quality. In this demo we present ARdoc, a tool which combines three techniques: (1) Natural Language Parsing (NLP), (2) Text Analysis (TA) and (3) Sentiment Analysis (SA) to automatically classify useful feedback contained in app reviews important for performing software maintenance and evolution tasks. Our quantitative and qualitative analysis (involving mobile professional developers) demonstrate that ARdoc correctly classifies feedback useful for maintenance perspectives in user reviews with high precision (ranging between 84% and 89%), recall (ranging between 84% and 89%), and an F-Measure (ranging between 84% and 89%). While evaluating our tool we also found that ARdoc substantially helps to extract important maintenance tasks for real world applications. Demo URL: https://youtu.be/Baf18V6sN8E Demo Web

Software maintenance is crucial for software projects evolution and success: code should be kept up-to-date and error-free, this with little effort and continuous updates for the end-users. In this context, issue trackers are essential tools for creating, managing and addressing the several (often hundreds of) issues that occur in software systems. A critical aspect for handling and prioritizing issues involves the assignment of labels to them (e.g., for projects hosted on GitHub), in order to determine the type (e.g., bug report, feature request and so on) of each specific issue. Although this labeling process has a positive impact on the effectiveness of issue processing, the current labeling mechanism is scarcely used on GitHub. In this demo, we introduce a tool, called Ticket Tagger, which leverages machine learning strategies on issue titles and descriptions for automatically labeling GitHub issues. Ticket Tagger automatically predicts the labels to assign to issues, with the aim of stimulating the use of labeling mechanisms in software projects, this to facilitate the issue management and prioritization processes. Along with the presentation of the tool's architecture and usage, we also evaluate its effectiveness in performing the issue labeling/classification process, which is critical to help maintainers to keep control of their workloads by focusing on the most critical issue tickets.