Can data transformation help in the detection of fault-prone modules?

Yue Jiang, Bojan Cukic, Tim Menzies
2008 Proceedings of the 2008 workshop on Defects in large software systems - DEFECTS '08  
Data preprocessing (transformation) plays an important role in data mining and machine learning. In this study, we investigate the effect of four different preprocessing methods to fault-proneness prediction using nine datasets from NASA Metrics Data Programs (MDP) and ten classification algorithms. Our experiments indicate that log transformation rarely improves classification performance, but discretization affects the performance of many different algorithms. The impact of different
more » ... ations differs. Random forest algorithm, for example, performs better with original and log transformed data set. Boosting and NaiveBayes perform significantly better with discretized data. We conclude that no general benefit can be expected from data transformations. Instead, selected transformation techniques are recommended to boost the performance of specific classification algorithms.
doi:10.1145/1390817.1390822 dblp:conf/issta/JiangCM08 fatcat:edbyqgno75a3jd42k3uiktwxy4