Empirical Evaluation of Machine Learning Algorithms for Fault Prediction

Arvinder Kaur, Inderpreet Kaur
2014 Lecture Notes on Software Engineering  
Producing quality software is a very challenging task looking at the size and complexity of software developed these days. Predicting software quality early helps in using testing resources optimally. So, many statistical and machine learning techniques are used to predict quality classes in software. In this work, six machine learning classifiers have been used to estimate the fault proneness of 5885 classes used in five open source software on the basis of object-oriented metrics calculated
more » ... etrics calculated on these classes. Bagging and J48 classifiers turn out to be the best one amongst the classifiers used.
doi:10.7763/lnse.2014.v2.118 fatcat:d6tohjgk2fg2tb23ksadfgbdhu