An Outlier Detection Algorithm Based on Object-Oriented Metrics Thresholds

Detection of outliers in software measurement datasets is a critical issue that affects the performance of software fault prediction models built based on these datasets. Two necessary components of fault prediction models, software metrics and fault data, are collected from the software projects developed with object-oriented programming paradigm. We proposed an outlier detection algorithm based on these kinds of metrics thresholds. We used Random Forests machine learning classifier on two software measurement datasets collected from jEdit open-source text editor project and experiments revealed that our outlier detection approach improves the performance of fault predictors based on Random Forests classifier.