Graph-based machine learning improves just-in-time defect prediction

HIGHLIGHTS

• who: Jonathan Bryan and Pablo Moriano from the Division, Oak Ridge National Laboratory, Oak Ridge, TN, United States of America Editor: have published the article: Graph-based machine learning improves just-in-time defect prediction, in the Journal: PLOS ONE of 24/06/2022
• what: By leveraging these contribution graphs the research shows the potential of using graph-based ML to improve Just-In-Time (JIT) defect prediction. The authors show the potential of using this approach with higher classification results (i.e., with a 152% higher F1 score and a 3% higher MCC) compared . . .
  

   

  

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