Understanding the Impact of GitHub Suggested Changes on Recommendations between Developers
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 NovDisplayed time zone: (UTC) Coordinated Universal Time change
17:30 - 18:00 | RecommendationStudent Research Competition / Research Papers / Paper Presentations at Virtual room 1 | ||
17:30 2mTalk | 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 1mTalk | 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 1mTalk | 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 1mTalk | 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 Prasad Fujitsu Labs, USA DOI Pre-print Media Attached | ||
17:39 1mTalk | Recommender Systems: Metric Suggestion Mechanisms Applied to Adaptable Software Dashboards Student Research Competition Dragos Strugar Innopolis University, Russia DOI | ||
17:41 1mTalk | Understanding the Impact of GitHub Suggested Changes on Recommendations between Developers Research Papers DOI | ||
17:43 17mTalk | 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 | ||