Popular open-source software projects receive and review contributions from a diverse array of developers, many of whom have little to no prior involvement with the project. A recent survey reported that reviewers con...
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Popular open-source software projects receive and review contributions from a diverse array of developers, many of whom have little to no prior involvement with the project. A recent survey reported that reviewers consider conformance to the project's code style to be one of the top priorities when evaluating code contributions on Github. We propose to quantitatively evaluate the existence and effects of this phenomenon. To this aim we use language models, which were shown to accurately capture stylistic aspects of code. We find that rejected change sets do contain code significantly less similar to the project than accepted ones, furthermore, the less similar change sets are more likely to be subject to thorough review. Armed with these results we further investigate whether new contributors learn to conform to the project style and find that experience is positively correlated with conformance to the project's code style.
After working for some time, developers commit their code changes to a version control system. When doing so, they often bundle unrelated changes (e.g., bug fix and refactoring) in a single commit, thus creating a so-...
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After working for some time, developers commit their code changes to a version control system. When doing so, they often bundle unrelated changes (e.g., bug fix and refactoring) in a single commit, thus creating a so-called tangled commit. Sharing tangled commits is problematic because it makes review, reversion, and integration of these commits harder and historical analyses of the project less reliable. researchers have worked at untangling existing commits, i.e., finding which part of a commit relates to which task. In this paper, we contribute to this line of work in two ways: (1) A publicly available dataset of untangled code changes, created with the help of two developers who accurately split their code changes into self contained tasks over a period of four months; (2) a novel approach, EpiceaUntangler, to help developers share untangled commits (aka. atomic commits) by using fine-grained code change information. EpiceaUntangler is based and tested on the publicly available dataset, and further evaluated by deploying it to 7 developers, who used it for 2 weeks. We recorded a median success rate of 91% and average one of 75%, in automatically creating clusters of untangled fine-grained code changes.
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