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Optimizing modern code review through recommendation algorithms

University of British Columbia

Software developers have many tools at their disposal that use a variety of sophisticated technology, such as static analysis and model checking, to help find defects before software is released. Despite the availability of such tools, software development still relies largely on human inspection of code to find defects. Many software development projects use code reviews as a means to ensure this human inspection occurs before a commit is merged into the system. Known as modern code review, this approach is based on tools, such as Gerrit, that help developers track commits for which review is needed and that help perform reviews asynchronously. As part of this approach, developers are often presented with a list of open code reviews requiring attention. Existing code review tools simply order this list of open reviews based on the last update time of the review; it is left to a developer to find a suitable review on which to work from a long list of reviews. In this thesis, we present an investigation of four algorithms that recommend an ordering of the list of open reviews based on properties of the reviews. We use a simulation study over a dataset of six projects from the Eclipse Foundation to show that an algorithm based on ordering reviews from least lines of code modified in the changes to be reviewed to most lines of code modified out performs other algorithms. This algorithm shows promise for eliminating stagnation of reviews and optimizing the average duration reviews are open.

Text

2016-08-12

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/58757

Computer Science

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/