Today\u2019s software development is distributed and involves continuous
\nchanges for new features and yet, their development cycle
\nhas to be fast and agile. An important component of enabling this
\nagility is selecting the right reviewers for every code-change – the
\nsmallest unit of development cycle. Modern tool based code review
\nis proven to be an effective way to achieve appropriate code review
\nof software changes. However, the selection of reviewers in modern
\ntool based code review systems is at best manual. As software and
\nteams scale, this poses the challenge of selecting the right reviewers,
\nwhich in turn determines software quality over time. While
\nprevious works have suggested automatic approaches to code reviewer
\nrecommendations, these have been limited to retrospective
\nanalysis. We not only deploy a reviewer suggestions algorithm –
\nWhoDo and evaluate its effect but also incorporate load balancing
\nas part of it to address one of its major shortcomings – of recommending
\nexperienced developers very frequently. We evaluate the
\neffect of this hybrid recommendation + load balancing system on
\nfive repositories within Microsoft. Our results are based around
\nvarious aspects of a changelist and how code review affects that.
\nWe attempt to quantitatively answer questions which are supposed
\nto play a vital role in effective code review through our data and
\nsubstantiate it through qualitative feedback of partner repositories.<\/p>\n","protected":false},"excerpt":{"rendered":"
Today\u2019s software development is distributed and involves continuous changes for new features and yet, their development cycle has to be fast and agile. An important component of enabling this agility is selecting the right reviewers for every code-change – the smallest unit of development cycle. 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