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ICSE 2021
Mon 17 May - Sat 5 June 2021

Crowdsourced testing is increasingly dominant in mobile application (app) testing, but it is a great burden for app developers to inspect the incredible number of test reports. Many researches have been proposed to deal with test reports based only on texts or additionally simple image features. However, in mobile app testing, texts contained in test reports are condensed and the information is inadequate. Many screenshots are included as complements that contain much richer information beyond texts. This trend motivates us to prioritize crowdsourced test reports based on a deep screenshot understanding.

In this paper, we present a novel crowdsourced test report prioritization approach, namely DeepPrior. We first represent the crowdsourced test reports with a novelly introduced feature, namely DeepFeature, that includes all the widgets along with their texts, coordinates, types, and even intents based on the deep analysis of the app screenshots, and the textual descriptions in the crowdsourced test reports. DeepFeature includes the Bug Feature, which directly describes the bugs, and the Context Feature, which depicts the thorough context of the bug. The similarity of the DeepFeature is used to represent the test reports’ similarity and prioritize the crowdsourced test reports. We formally define the similarity as DeepSimilarity. We also conduct an empirical experiment to evaluate the effectiveness of the proposed technique with a large dataset group. The results show that DeepPrior is promising, and it outperforms the state-of-the-art approach with less than half the overhead.

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: