Understanding the Impact of Feedback on Knowledge Sharing in Modern Code Review
Nargis Fatima, Sumaira Nazir, Suriayati bt Chuprat · 2019 IEEE 6th International Conference on Engineering Technologies and Applied Sciences (ICETAS) · 2019
Modern Code Review (MCR) is a key practice to improve code quality and share knowledge among authors and reviewers. Feedback from the reviewer is an important aspect of the review process that initiates the process of knowledge sharing. In MCR the knowledge sharing though reported as an important benefit, however also reported as a challenge for author and reviewer. It is argued that the reviewers do not share knowledge in the form of insightful, constructive and formative feedback. Developers are facing poor knowledge sharing challenge in addition to other MCR challenges. It is understood that the feedback from the reviewer is an important facet to share knowledge. There are various factors associated with the feedback provided by reviewers that hamper the knowledge sharing aspect in MCR process. There is a gap in the MCR literature concerning the exploration of feedback conceptualization, importance and its impacts on knowledge sharing. Therefore, this study explores the factors associated with feedback that impact knowledge sharing in MCR. Systematic Literature Review (SLR), grounded theory and expert opinion strategies are followed to recognize the unique, categorized and validated list of feedback concerning factors that impact knowledge sharing in MCR. The examination discovers 42 factors such as feedback usefulness, harsh comments, criticism on the author, broadcast feedback, etc. The identified 42 factors are grouped into 14 unique categories, for instance, feedback communication, feedback language, feedback content, feedback temporal aspects, etc. The performed study has suggestions for members involved in engaging in MCR activities and investigators attracted research this zone to widen the investigation and share knowledge adequately among team members by bearing in mind and coping with the negative effect of recognized variables and thus minimizing the production of software engineering wastes.