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Wed 11 Nov 2020 17:37 - 17:38 at Virtual room 1 - Recommendation

Using online Q&A forums, such as Stack Overflow (SO), for guidance to resolve program bugs, among other development issues, is commonplace in modern software development practice. Runtime exceptions (RE) is one such important class of bugs that is actively discussed on SO. In this work we present a technique and prototype tool called MAESTRO that can automatically recommend an SO post that is most relevant to a given Java RE in a developer's code. MAESTRO compares the exception-generating program scenario in the developer's code with that discussed in an SO post and returns the post with the closest match. To extract and compare the exception scenario effectively, MAESTRO first uses the answer code snippets in a post to implicate a subset of lines in the post's question code snippet as responsible for the exception and then compares these lines with the developer's code in terms of their respective Abstract Program Graph (APG) representations. The APG is a simplified and abstracted derivative of an abstract syntax tree, proposed in this work, that allows an effective comparison of the functionality embodied in the high-level program structure, while discarding many of the low-level syntactic or semantic differences. We evaluate MAESTRO on a benchmark of 78 instances of Java REs extracted from the top 500 Java projects on GitHub and show that MAESTRO can return either a highly relevant or somewhat relevant SO post corresponding to the exception instance in 71% of the cases, compared to relevant posts returned in only 8% - 44% instances, by four competitor tools based on state-of-the-art techniques. We also conduct a user experience study of MAESTRO with 10 Java developers, where the participants judge MAESTRO reporting a highly relevant or somewhat relevant post in 80% of the instances. In some cases the post is judged to be even better than the one manually found by the participant.

Wed 11 Nov

Displayed time zone: (UTC) Coordinated Universal Time change

17:30 - 18:00
17:30
2m
Talk
API Method Recommendation via Explicit Matching of Functionality Verb Phrases
Research Papers
Wenkai Xie Fudan University, China, Xin Peng Fudan University, China, Mingwei Liu Fudan University, China, Christoph Treude University of Adelaide, Australia, Zhenchang Xing Australian National University, Australia, Xiaoxin Zhang Fudan University, China, Wenyun Zhao Fudan University, China
DOI
17:33
1m
Talk
Code Recommendation for Exception Handling
Research Papers
Tam Nguyen Auburn University, USA, Phong Vu Auburn University, USA, Tung Nguyen Auburn University, USA
DOI
17:35
1m
Talk
eQual: Informing Early Design Decisions
Research Papers
Arman Shahbazian Google, USA, Suhrid Karthik University of Southern California, USA, Yuriy Brun University of Massachusetts Amherst, Nenad Medvidović University of Southern California, USA
Link to publication DOI Pre-print Media Attached
17:37
1m
Talk
Recommending Stack Overflow Posts for Fixing Runtime Exceptions using Failure Scenario Matching
Research Papers
Sonal Mahajan Fujitsu Labs, USA, Negarsadat Abolhassani University of Southern California, USA, Mukul R. Prasad Fujitsu Labs, USA
DOI Pre-print Media Attached
17:39
1m
Talk
Recommender Systems: Metric Suggestion Mechanisms Applied to Adaptable Software Dashboards
Student Research Competition
Dragos Strugar Innopolis University, Russia
DOI
17:41
1m
Talk
Understanding the Impact of GitHub Suggested Changes on Recommendations between Developers
Research Papers
Chris Brown North Carolina State University, USA, Chris Parnin North Carolina State University, USA
DOI
17:43
17m
Talk
Conversations on Recommendation
Paper Presentations
Chris Brown North Carolina State University, USA, Dragos Strugar Innopolis University, Russia, Mingwei Liu Fudan University, China, Sonal Mahajan Fujitsu Labs, USA, Arman Shahbazian University of Southern California, M: Massimiliano Di Penta University of Sannio, Italy