Knowledge sharing factors for modern code review to minimize software engineering waste

Software engineering activities, for instance, Modern Code Review (MCR) produce quality software by identifying the defects from the code. It involves social coding and provides ample opportunities to share knowledge among MCR team members. However, the MCR team is confronted with the issue of waiting waste due to poor knowledge sharing among MCR team members. As a result, it delays the project delays and increases mental distress. To minimize the waiting waste, this study aims to identify knowledge sharing factors that impact knowledge sharing in MCR. The methodology employed for this study is a systematic literature review to identify knowledge sharing factors, data coding with continual comparison and memoing techniques of grounded theory to produce a unique and categorized list of factors influencing knowledge sharing. The identified factors were then assessed through expert panel for its naming, expressions, and categorization. The study finding reported 22 factors grouped into 5 broad categories i.e. Individual, Team, Social, Facility conditions, and Artifact. The study is useful for researchers to extend the research and for the MCR team to consider these factors to enhance knowledge sharing and to minimize waiting waste

Automate Code Review KPI and analyse correlations with the generated values

Code review, also known as peer code review, is the deliberate and methodical gathering of one's fellow programmers to examine each other's code for errors. This thesis was done at a company that required a code review KPI (Key Performance Indicator) moni toring tool to be built. The company has standard code review process, and they want to encourage all the teams to follow the code review process regularly. To verify the regu larity, the management requires the code review KPI for different repositories. It takes a lot of time to calculate the KPI value manually. The requirement was to prepare the KPI tool and visualize the values graphically for their internal use. Besides this, the purpose of this thesis was to analyze if there were any correlations available between the KPI values or some other information in their repositories. The tool was successfully developed as a new product, and users have already started to use it. A survey was conducted after the implementation of the tool among the team to which I relate, and there were some valuable responses found from this. Research has been performed with a total of one year of KPI (Key Performance Indicator) values and some repositories’ information to find correlations. Some data from SonarQube was also included in the research data. The result showed some correlations between some concrete values (the number of unreviewed code updates and the number of corrected bugs). There were some other correlations found with KPI values which we discussed in this thesis. The company was also looking for some answers on whether the code review was worth it. The effectiveness of code review has been discussed by considering the correlations found from the analysis