Predicting Defectiveness of Software Patches

October 19, 2016
2:00 pm - 5:30 pm
Hall C

Track: General
Type: Posters
Level: All

Software code review is the inspection of code change in order to find possible defects and ensure change quality. However, reviews may not guarantee finding defects. This research aims to predict the defectiveness of reviewed code change applying several machine learning algorithms. Our empirical results show that Bayesian Network predicts code change defectiveness with comparing high accuracy.


, PhD student, Ryerson University