Abstract
Code reviews consist in proof-reading proposed code changes in order to find their shortcomings such as bugs, insufficient test coverage or misused design patterns. Code reviews are conducted before merging submitted changes into the main development branch. The selection of suitable reviewers is crucial to obtain the high quality of reviews. In this article we present a new method of recommending reviewers for code changes. This method is based on profiles of individual programmers. For each developer we maintain his/her profile. It is the multiset of all file path segments from commits reviewed by him/her. It will get updated when he/she presents a new review. We employ a similarity function between such profiles and change proposals to be reviewed. The programmer whose profile matches the change most is recommended to become the reviewer. We performed an experimental comparison of our method against state-of-the-art techniques using four large open-source projects. We obtained improved results in terms of classification metrics (precision, recall and F-measure) and performance (we have lower time and space complexity).
Highlights
Software quality is one of the main concerns of software engineering
The knowledge about the project can be reinforced by code reviews (Nelson and Schumann 2004)
We focus on the automation of the code reviewer selection
Summary
Software quality is one of the main concerns of software engineering. Numerous systematic approaches to assure this quality exist and are applied. The practice of peer code reviews understood as reading and evaluating quality of code changes by other developers is known from 1960s (Bisant and Lyle 1989) Since that time it has played an important role in the life cycle of numerous projects. When done properly, it may help spreading knowledge among developers and encouraging the emergence of code standards for the project (Nelson and Schumann 2004). It may help spreading knowledge among developers and encouraging the emergence of code standards for the project (Nelson and Schumann 2004) It affects the final quality of the project as better code review coverage results in less post-release defects (McIntosh et al 2014). Some may prefer automated code review systems, like Gerrit, GitLab, Phabricator, Review Board, Rietveld or Rhodecode, that can be configured to enable merging source code changes into the main repository only when the code review is successful
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