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Wed 11 Nov 2020 17:41 - 17:42 at Virtual room 1 - Recommendation

Recommendations between colleagues are effective for encouraging developers to adopt better practices. Research shows these peer interactions are useful for improving \textit{developer behaviors}, or the adoption of activities to help software engineers complete programming tasks. However, in-person recommendations between developers in the workplace are declining. One form of online recommendations between developers are pull requests, which allow users to propose code changes and provide feedback on contributions. GitHub, a popular code hosting platform, recently introduced the \textit{suggested changes} feature, which allows users to recommend improvements for pull requests. To better understand this feature and its impact on recommendations between developers, we report an empirical study of this system, measuring usage, effectiveness, and perception. Our results show that suggested changes support code review activities and significantly impact the timing and communication between developers on pull requests. This work provides insight into the suggested changes feature and implications for improving future systems for automated developer recommendations, such as providing situated, concise, and actionable feedback.

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