Linear and non-linear heterogeneous ensemble methods to predict the number of faults in software systems

This paper expands the use of ensemble methods for the prediction of number of faults unlikely the earlier works on ensemble methods that focused on predicting software modules as faulty or non-faulty.This paper investigates the usage of both heterogeneous ensemble methods as well as homogeneous ensemble methods for the prediction of number of faults.We present two linear combination rules and two non-linear combination rules for combining the outputs of the base learners in the ensemble.In addition, we assess the performance of ensemble methods under two different scenarios, intra-release prediction and inter-releases prediction.The experiments are performed over five open-source software systems with their fifteen releases, collected from the PROMISE data repository. Several classification techniques have been investigated and evaluated earlier for the software fault prediction. These techniques have produced different prediction accuracy for the different software systems and none of the technique has always performed consistently better across different domains. On the other hand, software fault prediction using ensemble methods can be very effective, as they take the advantage of each participating technique for the given dataset and try to come up with better prediction results compared to the individual techniques. Many works are available for classifying software modules being faulty or non-faulty using the ensemble methods. These works are only specifying that whether a given software module is faulty or not, but number of faults in that module are not predicted by them. The use of ensemble methods for the prediction of number of faults has not been explored so far. To fulfill this gap, this paper presents ensemble methods for the prediction of number of faults in the given software modules. The experimental study is designed and conducted for five open-source software projects with their fifteen releases, collected from the PROMISE data repository. The results are evaluated under two different scenarios, intra-release prediction and inter-releases prediction. The prediction accuracy of ensemble methods is evaluated using absolute error, relative error, prediction at level l, and measure of completeness performance measures. Results show that the presented ensemble methods yield improved prediction accuracy over the individual fault prediction techniques under consideration. Further, the results are consistent for all the used datasets. The evidences obtained from the prediction at level l and measure of completeness analysis have also confirmed the effectiveness of the proposed ensemble methods for predicting the number of faults.