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ICSE 2021
Sun 16 May - Sat 5 June 2021

This program is tentative and subject to change.

Millions of mobile apps have been available through various app markets. Although most app markets have enforced a number of automated or even manual mechanisms to vet each app before it is released to the market, thousands of low-quality apps still exist in different markets, some of which violate the explicitly specified market policies. In order to identify these violations accurately and timely, we resort to user comments, which can form an immediate feedback for app market maintainers, to identify undesired behaviors that violate market policies, including security-related user concerns. Specifically, we present the first large-scale study to detect and characterize the correlations between user comments and market policies. First, we propose CHAMP, an approach that adopts text mining and natural language processing (NLP) techniques to extract semantic rules through a semi-automated process, and classifies comments into 26 pre-defined types of undesired behaviors that violate market policies. Our evaluation on real-world user comments shows that it achieves both high precision and recall (> 0.9) in classifying comments for undesired behaviors. Then, we curate a large-scale comment dataset (over 3 million user comments) from apps in Google Play and 8 popular alternative Android app markets, and apply CHAMP to understand the characteristics of undesired behavior comments in the wild. The results confirm our speculation that user comments can be used to pinpoint suspicious apps that violate policies declared by app markets. The study also reveals that policy violations are widespread in many app markets despite their extensive vetting efforts. CHAMP can be a whistle blower that assigns policy-violation scores and identifies most informative comments for apps.

This program is tentative and subject to change.

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
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