Software Fault Prediction Using Data Mining Techniques on Software Metrics

Abstract

Software industries have enormous demand for fault prediction of the faulty module and fault removal techniques. Many researchers have developed different fault prediction models to predict the fault at an early stage of the software development life cycle (SDLC). But the state-of-the-art model still suffers from the performance and generalize validation of the models. However, some researchers refer to data mining techniques, machine learning, and artificial intelligence play crucial roles in developing fault prediction models. A recent study stated that metric selection techniques also help to enhance the performance of models. Hence, to resolve the issue of improving the fault prediction model’s performance and validation, we have used data mining, instance selection, metric selection, and ensemble methods to beat the state-of-the-art results. For the validation, we have collected the 22 software projects from the four different software repositories. We have implemented three machine learning algorithms and three ensemble methods with two metric selection methods on 22 datasets. The statistical evaluation of the implemented model performed using Wilcoxon signed-rank test and the Friedman test followed by the Nemenyi test to find the significant model. As a result, the Random forest algorithm produces the best result with an average median of 95.43% (accuracy) and 0.96 (f-measure) on 22 software projects. Based on the Nemenyi test, Random forest (RF) is performing better with 4.54 (accuracy mean score) and 4.41 (f-measure mean score) shown in the critical diagram. Experimental study shows that data mining techniques with PCA provide better accuracy and f-measure.