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ICSE 2020
Wed 24 June - Thu 16 July 2020
Fri 10 Jul 2020 16:05 - 16:17 at Goguryeo - A23-Requirements Chair(s): Dalal Alrajeh

App reviews can provide important intelligence that app developers can apply to improve their offerings. Whereas previous research on review analysis has considered the easily extractable elements of an app review (such as topics and sentiment), it has largely ignored the more subtle - and potentially more informative - elements. Specifically, a user’s review of an app would often describe the user’s interactions with the app. These interactions, which we interpret as mini stories, are prominent in reviews with negative ratings.

In general, a story in an app review would contain at least two types of events: (1) user actions, indicative of use cases or user expectations and (2) associated app behaviors, indicative of problems that violate the user’s expectations. Being able to identify such stories would enable a developer in better maintaining and improving his or her app’s functionality and enhancing user experience.

To this end, we present CASPAR, a method for collecting and analyzing user-reported mini stories regarding app problems from app reviews. CASPAR abstracts event pairs from stories in reviews. By extending and applying natural language processing and deep learning techniques, CASPAR extracts ordered events from app reviews, classifies them as user actions or app problems, and conducts inference on action-problem event pairs. It builds and trains an inference model with the extracted event pairs to predict possible app problems for different use cases.

CASPAR discovers high-quality event pairs regarding app problems from reviews, and infers plausible app problems for use cases. We conduct two main evaluations. First, CASPAR classifies the events with an accuracy of 82.0% on manually labeled data. Second, relative to human evaluators, CASPAR extracts event pairs with 92.9% precision and 28.9% recall, and infers events with high plausibility. Our dataset and code will be released upon acceptance.

Fri 10 Jul

Displayed time zone: (UTC) Coordinated Universal Time change

16:05 - 17:05
A23-RequirementsJournal First / Technical Papers / New Ideas and Emerging Results at Goguryeo
Chair(s): Dalal Alrajeh Imperial College London
Caspar: Extracting and Synthesizing User Stories of Problems from App ReviewsTechnical
Technical Papers
Hui Guo North Carolina State University, Munindar P. Singh North Carolina State University
Dealing with Non-Functional Requirements in Model-Driven Development: A SurveyJ1
Journal First
David Ameller Universitat Politècnica de Catalunya, Xavier Franch Universitat Politècnica de Catalunya, Cristina Gómez Universitat Politècnica de Catalunya, Silverio Martínez-Fernández UPC-BarcelonaTech, João Araújo Universidade Nova de Lisboa, Stefan Biffl Vienna University of Technology, Jordi Cabot ICREA - UOC, Vittorio Cortellesa University of L’Aquila, Daniel Mendez Technische Universität München, Ana Moreira FCT / Universidade Nova de Lisboa, Henry Muccini University of L'Aquila, Italy, Antonio Vallecillo University of Málaga, Spain, Manuel Wimmer Johannes Kepler University Linz, Vasco Amaral Universidade Nova de Lisboa, Wolfang Böhm Technische Universität München, Hugo Brunelière Inria, Mines Nantes & LINA, Lola Burgueño Universidad de Malaga, Miguel Goulao NOVA-LINCS, FCT/UNL, Sabine Teufl Fortiss GmbH, Luca Berardinelli Johannes Kepler University Linz
Locating Latent Design Information in Developer Discussions: A Study on Pull RequestsJ1
Journal First
Giovanni Viviani University of British Columbia, Michalis Famelis Université de Montréal, Xin Xia Monash University, Calahan Janik-Jones University of Toronto, Gail Murphy University of British Columbia
Status Quo in Requirements Engineering: A Theory and a Global Family of SurveysJ1
Journal First
Stefan Wagner University of Stuttgart
Link to publication DOI Pre-print
Corba: Crowdsourcing to Obtain Requirements from Regulations and BreachesJ1
Journal First
Hui Guo North Carolina State University, Ozgur Kafali University of Kent, Anne-Liz Jeukeng University of Florida, Laurie Williams North Carolina State University, Munindar P. Singh North Carolina State University
With Registered Reports Towards Large Scale Data CurationNIER
New Ideas and Emerging Results
Steffen Herbold University of Göttingen