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- W3032501809 abstract "Source code is changed for a reason, e.g., to adapt, correct, or adapt it. This reason can provide valuable insight into the development process but is rarely explicitly documented when the change is committed to a source code repository. Automatic commit classification uses features extracted from commits to estimate this reason. We introduce source code density, a measure of the net size of a commit, and show how it improves the accuracy of automatic commit classification compared to previous size-based classifications. We also investigate how preceding generations of commits affect the class of a commit, and whether taking the code density of previous commits into account can improve the accuracy further. We achieve up to 89% accuracy and a Kappa of 0.82 for the cross-project commit classification where the model is trained on one project and applied to other projects. Models trained on single projects yield accuracies of up to 93% with a Kappa approaching 0.90. The accuracy of the automatic commit classification has a direct impact on software (process) quality analyses that exploit the classification, so our improvements to the accuracy will also improve the confidence in such analyses." @default.
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- W3032501809 date "2020-10-01" @default.
- W3032501809 modified "2023-09-27" @default.
- W3032501809 title "Using source code density to improve the accuracy of automatic commit classification into maintenance activities" @default.
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- W3032501809 doi "https://doi.org/10.1016/j.jss.2020.110673" @default.
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