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ICSE 2021
Mon 17 May - Sat 5 June 2021

When a bug manifests in a user-facing application, it is likely to be exposed through the graphical user interface (GUI). Given the importance of visual information to the process of identifying and understanding such bugs, users are increasingly making use of screenshots and screen-recordings as a means to report issues to developers. However, when such information is reported en masse, such as during crowd-sourced testing, managing these artifacts can be a time-consuming process. As the reporting of screen-recordings in particular becomes more popular, developers are likely to face challenges related to manually identifying videos that depict duplicate bugs. Due to their graphical nature, screen-recordings present challenges for automated analysis that preclude the use of current duplicate bug report detection techniques. To overcome these challenges and aid developers in this task, this paper presents Tango, a duplicate detection technique that operates purely on video-based bug reports by leveraging both visual and textual information. Tango processes this data using a combination of tailored computer vision techniques, optical character recognition, and text retrieval. We evaluated multiple configurations of Tango in a comprehensive empirical evaluation on 4,860 duplicate detection tasks that involved a total of 180 screen-recordings from six Android apps. Additionally, we conducted a user study investigating the effort required for developers to manually detect duplicate video-based bug reports and compared this to the effort required to use Tango. The results reveal that Tango’s optimal configuration is highly effective at detecting duplicate video-based bug reports, accurately ranking target duplicate videos in the top-2 returned results in 83% of cases. Additionally, our user study shows that, on average, Tango can reduce developer effort by over 60%, illustrating its practicality.

Conference Day
Tue 25 May

Displayed time zone: Amsterdam, Berlin, Bern, Rome, Stockholm, Vienna change

15:20 - 16:15
1.3.4. Obtaining Information from App User Reviews #2Technical Track / SEIS - Software Engineering in Society at Blended Sessions Room 4 +12h
Chair(s): Birgit PenzenstadlerChalmers
15:20
15m
Paper
Does Culture Matter? Impact of Individualism and Uncertainty Avoidance on App ReviewsSEIS
SEIS - Software Engineering in Society
Ricarda Anna-Lena FischerMaastricht University, Rita WalczuchMaastricht University, Emitzá GuzmánVrije Universiteit Amsterdam
Pre-print Media Attached
15:35
20m
Paper
Automatically Matching Bug Reports With Related App ReviewsTechnical Track
Technical Track
Marlo HaeringUniversity of Hamburg, Germany, Christoph StanikUniversity of Hamburg, Germany, Walid MaalejUniversity of Hamburg, Germany
Pre-print Media Attached
15:55
20m
Paper
It Takes Two to Tango: Combining Visual and Textual Information for Detecting Duplicate Video-Based Bug ReportsArtifact ReusableTechnical Track
Technical Track
Nathan CooperWilliam & Mary, Carlos Bernal-CárdenasMicrosoft, Oscar ChaparroCollege of William & Mary, Kevin MoranGeorge Mason University, Denys PoshyvanykCollege of William & Mary
Pre-print Media Attached

Conference Day
Wed 26 May

Displayed time zone: Amsterdam, Berlin, Bern, Rome, Stockholm, Vienna change

03:20 - 04:15
1.3.4. Obtaining Information from App User Reviews #2Technical Track / SEIS - Software Engineering in Society at Blended Sessions Room 4
03:20
15m
Paper
Does Culture Matter? Impact of Individualism and Uncertainty Avoidance on App ReviewsSEIS
SEIS - Software Engineering in Society
Ricarda Anna-Lena FischerMaastricht University, Rita WalczuchMaastricht University, Emitzá GuzmánVrije Universiteit Amsterdam
Pre-print Media Attached
03:35
20m
Paper
Automatically Matching Bug Reports With Related App ReviewsTechnical Track
Technical Track
Marlo HaeringUniversity of Hamburg, Germany, Christoph StanikUniversity of Hamburg, Germany, Walid MaalejUniversity of Hamburg, Germany
Pre-print Media Attached
03:55
20m
Paper
It Takes Two to Tango: Combining Visual and Textual Information for Detecting Duplicate Video-Based Bug ReportsArtifact ReusableTechnical Track
Technical Track
Nathan CooperWilliam & Mary, Carlos Bernal-CárdenasMicrosoft, Oscar ChaparroCollege of William & Mary, Kevin MoranGeorge Mason University, Denys PoshyvanykCollege of William & Mary
Pre-print Media Attached