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ICSE 2021
Mon 17 May - Sat 5 June 2021

Due to the rapid growth and strong competition of mobile application (app) market, app developers should not only offer users with attractive new features, but also carefully maintain and improve existing features based on users’ feedbacks. User reviews indicate a rich source of information to plan such feature maintenance activities, and it could be of great benefit for developers to evaluate and magnify the contribution of specific features to the overall success of their apps. In this study, we refer to the features that are highly correlated to app ratings as key features, and we present KEFE, a novel approach that leverages app description and user reviews to identify key features of a given app. The application of KEFE especially relies on natural language processing, deep machine learning classifier, and regression analysis technique, which involves three main steps: 1) extracting feature-describing phrases from app description; 2) matching each app feature with its relevant user reviews; and 3) building a regression model to identify features that have significant relationships with app ratings. To train and evaluate KEFE, we collect 200 app descriptions and 1,108,148 user reviews from Chinese Apple App Store. Experimental results demonstrate the effectiveness of KEFE in feature extraction, where an average F-measure of 78.13% is achieved. The key features identified are also likely to provide hints for successful app releases, as for the releases that receive higher app ratings, 70% of features improvements are related to key features.

Tue 25 May
Times are displayed in time zone: Amsterdam, Berlin, Bern, Rome, Stockholm, Vienna change

10:30 - 11:30
1.1.4. Obtaining Information from App User Reviews #1Technical Track at Blended Sessions Room 4 +12h
Chair(s): Patricia LagoVrije Universiteit Amsterdam
10:30
20m
Paper
Identifying Key Features from App User ReviewsTechnical Track
Technical Track
Huayao WuNanjing University, Wenjun DengNanjing University, Xintao NiuNanjing University, Changhai NieNanjing University
Pre-print
10:50
20m
Paper
CHAMP: Characterizing Undesired App Behaviors from User Comments based on Market PoliciesTechnical Track
Technical Track
Yangyu HuChongqing University of Posts and Telecommunications, Haoyu WangBeijing University of Posts and Telecommunications, Tiantong JiCase Western Reserve University, Xusheng XiaoCase Western Reserve University, Xiapu LuoThe Hong Kong Polytechnic University, Peng GaoUniversity of California, Berkeley, Yao GuoPeking University
Pre-print
11:10
20m
Paper
Prioritize Crowdsourced Test Reports via Deep Screenshot UnderstandingTechnical Track
Technical Track
Shengcheng YuNanjing University, Chunrong FangNanjing University, Zhenfei CaoNanjing University, Xu WangNanjing University, Tongyu LiNanjing University, Zhenyu ChenNanjing University
Pre-print
22:30 - 23:30
1.1.4. Obtaining Information from App User Reviews #1Technical Track at Blended Sessions Room 4
22:30
20m
Paper
Identifying Key Features from App User ReviewsTechnical Track
Technical Track
Huayao WuNanjing University, Wenjun DengNanjing University, Xintao NiuNanjing University, Changhai NieNanjing University
Pre-print
22:50
20m
Paper
CHAMP: Characterizing Undesired App Behaviors from User Comments based on Market PoliciesTechnical Track
Technical Track
Yangyu HuChongqing University of Posts and Telecommunications, Haoyu WangBeijing University of Posts and Telecommunications, Tiantong JiCase Western Reserve University, Xusheng XiaoCase Western Reserve University, Xiapu LuoThe Hong Kong Polytechnic University, Peng GaoUniversity of California, Berkeley, Yao GuoPeking University
Pre-print
23:10
20m
Paper
Prioritize Crowdsourced Test Reports via Deep Screenshot UnderstandingTechnical Track
Technical Track
Shengcheng YuNanjing University, Chunrong FangNanjing University, Zhenfei CaoNanjing University, Xu WangNanjing University, Tongyu LiNanjing University, Zhenyu ChenNanjing University
Pre-print

Information for Participants
Info for Blended Sessions Room 4: