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Mitigating Turnover with Code Review Recommendation: Balancing Expertise, Workload, and Knowledge Distribution

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Mitigating Turnover with Code Review Recommendation: Balancing Expertise, Workload, and Knowledge Distribution

MirsaeediFarahani, SeyyedEhsanEdin (2019) Mitigating Turnover with Code Review Recommendation: Balancing Expertise, Workload, and Knowledge Distribution. Masters thesis, Concordia University.

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Abstract

Developer turnover is inevitable on software projects and leads to knowledge loss, a reduction in productivity, and an increase in defects. Mitigation strategies to deal with turnover tend to disrupt and increase workloads for developers. In this work, we suggest that through code review recommendation we can distribute knowledge and mitigate turnover with minimal impact on the development process. We evaluate review recommenders in the context of ensuring expertise during review, Expertise, reducing the review workload of the core team, CoreWorkload, and reducing the
Files at Risk to turnover, FaR. We find that prior work that assigns reviewers based on file ownership concentrates knowledge on a small group of core developers increasing risk of knowledge loss
from turnover by up to 65%. We propose learning and retention aware review recommenders that when combined are effective at reducing the risk of turnover by -29% but they unacceptably reduce
the overall expertise during reviews by -26%. We develop the Sophia recommender that suggest experts when none of the files under review are hoarded by developers but distributes knowledge when files are at risk. In this way, we are able to simultaneously increase expertise during review with a ΔExpertise of 6%, with a negligible impact on workload of ΔCoreWorkload of 0.09%, and reduce the files at risk by ΔFaR -28%. Sophia is integrated into GitHub pull requests allowing developers to select an appropriate expert or “learner” based on the context of the review. We release the Sophia bot as well as the code and data for replication purposes.

Item Type:Thesis (Masters)
Authors:MirsaeediFarahani, SeyyedEhsanEdin
Institution:Concordia University
Degree Name:M. Comp. Sc.
Program:Computer Science
Date:4 September 2019
Thesis Supervisor(s):Rigby, Peter
Keywords:code review, turnover, knowledge, truck factor
ID Code:985877
Deposited By: SeyyedEhsanEdin MirsaeediFarahani
Deposited On:06 Feb 2020 02:47
Last Modified:16 Feb 2021 23:59
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