Understanding shared links and their intentions to meet information needs in modern code review:: A case study of the OpenStack and Qt projects

Dong Wang, Tao Xiao, Patanamon Thongtanunam, Raula Gaikovina Kula, Kenichi Matsumoto

Research output: Contribution to journalArticlepeer-review

14 Citations (Scopus)

Abstract

Code reviews serve as a quality assurance activity for software teams. Especially for Modern Code Review, sharing a link during a review discussion serves as an effective awareness mechanism where “Code reviews are good FYIs [for your information].”. Although prior work has explored link sharing and the information needs of a code review, the extent to which links are used to properly conduct a review is unknown. In this study, we performed a mixed-method approach to investigate the practice of link sharing and their intentions. First, through a quantitative study of the OpenStack and Qt projects, we identify 19,268 reviews that have 39,686 links to explore the extent to which the links are shared, and analyze a correlation between link sharing and review time. Then in a qualitative study, we manually analyze 1,378 links to understand the role and usefulness of link sharing. Results indicate that internal links are more widely referred to (93% and 80% for the two projects). Importantly, although the majority of the internal links are referencing to reviews, bug reports and source code are also shared in review discussions. The statistical models show that the number of internal links as an explanatory factor does have an increasing relationship with the review time. Finally, we present seven intentions of link sharing, with providing context being the most common intention for sharing links. Based on the findings and a developer survey, we encourage the patch author to provide clear context and explore both internal and external resources, while the review team should continue link sharing activities. Future research directions include the investigation of causality between sharing links and the review process, as well as the potential for tool support.

Original languageEnglish
Article number96
JournalEmpirical Software Engineering
Volume26
Issue number5
DOIs
Publication statusPublished - Sept 2021
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Software

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