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Graph-Based Machine Learning Improves Just-in-Time Defect Prediction. (arXiv:2110.05371v2 [cs.SE] UPDATED)
stat.ML updates on arXiv.org arxiv.org
The increasing complexity of today's software requires the contribution of
thousands of developers. This complex collaboration structure makes developers
more likely to introduce defect-prone changes that lead to software faults.
Determining when these defect-prone changes are introduced has proven
challenging, and using traditional machine learning (ML) methods to make these
determinations seems to have reached a plateau. In this work, we build
contribution graphs consisting of developers and source files to capture the
nuanced complexity of changes required to build …
arxiv graph graph-based learning machine machine learning prediction time