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MSR 2021
Mon 17 - Wed 19 May 2021
co-located with ICSE 2021
Wed 19 May 2021 17:01 - 17:04 at MSR Room 1 - Energy, logging, and APIs Chair(s): Akond Rahman

Automatic crash reporting systems have become a de-facto standard in software development. These systems monitor target software, and if a crash occurs they send details to a backend application. Later on, these reports are aggregated and used in the development process to 1) understand whether it is a new or an existing issue, 2) assign these bugs to appropriate developers, and 3) gain a general overview of the application’s bug landscape. The efficiency of report aggregation and subsequent operations heavily depends on the quality of the report similarity metric. However, a distinctive feature of this kind of report is that no textual input from the user (i.e., bug description) is available: it contains only stack trace information.

In this paper, we present S3M (“extreme”)~— the first approach to computing stack trace similarity based on deep learning. It is based on a siamese architecture that uses a biLSTM encoder and a fully-connected classifier to compute similarity. Our experiments demonstrate the superiority of our approach over the state-of-the-art on both open-sourced data and a private JetBrains dataset. Additionally, we review the impact of stack trace trimming on the quality of the results.

Conference Day
Wed 19 May

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

17:00 - 17:50
Energy, logging, and APIsTechnical Papers at MSR Room 1
Chair(s): Akond RahmanTennessee Tech University
17:01
3m
Talk
S3M: Siamese Stack (Trace) Similarity Measure
Technical Papers
Aleksandr KhvorovJetBrains, ITMO University, Roman VasilievJetBrains, George ChernishevSaint-Petersburg State University, Irving Muller RodriguesPolytechnique Montreal, Montreal, Canada, Dmitrij KoznovSaint-Petersburg State University, Nikita PovarovJetBrains
Pre-print
17:04
4m
Talk
Mining the ROS ecosystem for Green Architectural Tactics in Robotics and an Empirical Evaluation
Technical Papers
Ivano MalavoltaVrije Universiteit Amsterdam, Katerina ChinnappanVrije Universiteit Amsterdam, Stan SwanbornVrije Universiteit Amsterdam, The Netherlands, Grace LewisCarnegie Mellon Software Engineering Institute, Patricia LagoVrije Universiteit Amsterdam
Pre-print Media Attached
17:08
4m
Talk
Mining Energy-Related Practices in Robotics Software
Technical Papers
Michel AlbonicoUTFPR, Ivano MalavoltaVrije Universiteit Amsterdam, Gustavo PintoFederal University of Pará, Emitzá GuzmánVrije Universiteit Amsterdam, Katerina ChinnappanVrije Universiteit Amsterdam, Patricia LagoVrije Universiteit Amsterdam
Pre-print Media Attached
17:12
3m
Talk
Mining API Interactions to Analyze Software Revisions for the Evolution of Energy Consumption
Technical Papers
Andreas SchulerUniversity of Applied Sciences Upper Austria, Gabriele Anderst-KotsisJohannes Kepler University, Linz, Austria
Pre-print
17:15
4m
Talk
Can I Solve it? Identifying the APIs required to complete OSS tasks
Technical Papers
Fabio Marcos De Abreu SantosNorthern Arizona University, USA, Igor Scaliante WieseFederal University of Technology – Paraná - UTFPR, Bianca TrinkenreichNorthern of Arizona Univeristy, Igor SteinmacherFederal University of Technology - Paraná / Northern Arizona University, Anita SarmaOregon State University, Marco GerosaNorthern Arizona University, USA
Pre-print
17:19
31m
Live Q&A
Discussions and Q&A
Technical Papers


Information for Participants
Info for MSR Room 1: