Write a Blog >>
ICSE 2021
Mon 17 May - Sat 5 June 2021

In the process of discovering bugs, developers can either find new or enhance existing bug reports by including user feedback. Users may not only discover bugs earlier but also add important context information or steps to reproduce when describing the problems they face. App stores allow users to give feedback on apps and developers to react to it. However, finding user feedback that matches existing bug reports is challenging. In this work, we introduce DeepMatcher, an automatic approach using state-of-the-art deep learning methods to match problem reports in app reviews to bug reports in issue trackers. We evaluate DeepMatcher with four open-source apps quantitatively and qualitatively. In our evaluation, DeepMatcher identified 167 matching bug reports for 200 problem reports with three suggestions per problem report. On average, DeepMatcher achieved a hit ratio of 0.71 and a Mean Average Precision of 0.55. For 91 problem reports, DeepMatcher did not find any matching bug report, which we analyzed manually. We qualitatively looked into the issue trackers of the studied apps and found that in 47 cases, users described a problem before developers discovered and documented it. Finally, we discuss different use cases of DeepMatcher to facilitate the bug fixing process for developers.

Tue 25 May
Times are displayed in 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
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
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-CárdenasWilliam and Mary, Oscar ChaparroCollege of William & Mary, Kevin MoranGeorge Mason University, Denys PoshyvanykCollege of William & Mary
Pre-print

Wed 26 May
Times are displayed in 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
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
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-CárdenasWilliam and Mary, Oscar ChaparroCollege of William & Mary, Kevin MoranGeorge Mason University, Denys PoshyvanykCollege of William & Mary
Pre-print

Information for Participants
Info for Blended Sessions Room 4: