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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.

Conference Day
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 AlrajehImperial College London
16:05
12m
Talk
Caspar: Extracting and Synthesizing User Stories of Problems from App ReviewsTechnical
Technical Papers
Hui GuoNorth Carolina State University, Munindar P. SinghNorth Carolina State University
16:17
8m
Talk
Dealing with Non-Functional Requirements in Model-Driven Development: A SurveyJ1
Journal First
David AmellerUniversitat Politècnica de Catalunya, Xavier FranchUniversitat Politècnica de Catalunya, Cristina GómezUniversitat Politècnica de Catalunya, Silverio Martínez-FernándezUPC-BarcelonaTech, João AraújoUniversidade Nova de Lisboa, Stefan BifflVienna University of Technology, Jordi CabotICREA - UOC, Vittorio CortellesaUniversity of L’Aquila, Daniel MendezTechnische Universität München, Ana MoreiraFCT / Universidade Nova de Lisboa, Henry MucciniUniversity of L'Aquila, Italy, Antonio VallecilloUniversity of Málaga, Spain, Manuel WimmerJohannes Kepler University Linz, Vasco AmaralUniversidade Nova de Lisboa, Wolfang BöhmTechnische Universität München, Hugo BrunelièreInria, Mines Nantes & LINA, Loli BurgueñoUniversidad de Malaga, Miguel GoulaoNOVA-LINCS, FCT/UNL, Sabine TeuflFortiss GmbH, Luca BerardinelliJohannes Kepler University Linz
16:25
8m
Talk
Locating Latent Design Information in Developer Discussions: A Study on Pull RequestsJ1
Journal First
Giovanni VivianiUniversity of British Columbia, Michalis FamelisUniversité de Montréal, Xin XiaMonash University, Calahan Janik-JonesUniversity of Toronto, Gail MurphyUniversity of British Columbia
16:33
8m
Talk
Status Quo in Requirements Engineering: A Theory and a Global Family of SurveysJ1
Journal First
Stefan WagnerUniversity of Stuttgart
Link to publication DOI Pre-print
16:41
8m
Talk
Corba: Crowdsourcing to Obtain Requirements from Regulations and BreachesJ1
Journal First
Hui GuoNorth Carolina State University, Ozgur KafaliUniversity of Kent, Anne-Liz JeukengUniversity of Florida, Laurie WilliamsNorth Carolina State University, Munindar P. SinghNorth Carolina State University
16:49
6m
Talk
With Registered Reports Towards Large Scale Data CurationNIER
New Ideas and Emerging Results
Steffen HerboldUniversity of Göttingen
Pre-print