A fuzzy-filtered neuro-fuzzy framework for software fault prediction for inter-version and inter-project evaluation

摘要

Fault Prediction is the most required measure to estimate the software quality and reliability. Several methods, measures, aspects and testing methodologies are available to evaluate the software fault. In this paper, a fuzzy-filtered neuro-fuzzy framework is introduced to predict the software faults for internal and external software projects. The suggested framework is split into three primary phases. At the earlier phase, the effective metrics or measures are identified, which can derive the accurate decision on prediction of software fault. In this phase, the composite analytical observation of each software attribute is calculated using Information Gain and Gain Ratio measures. In the second phase, these fuzzy rules are applied on these measures for selection of effective and high-impact features. In the last phase, the Neuro-fuzzy classifier is applied on fuzzy-filtered training and testing sets. The proposed framework is applied to identify the software faults based on inter-version and inter-project evaluation. In this framework, the earlier projects or project-versions are considered as training sets and the new projects or versions are taken as testing sets. The experimentation is conducted on nine open source projects taken from PROMISE repository as well as on PDE and JDT projects. The approximation is applied on internal version-specific fault prediction and external software projects evaluation. The comparative analysis is performed against Decision Tree, Random Tree, Random Forest, Naive Bayes and Multilevel Perceptron classifiers. This prediction result signifies that the proposed framework has gained the higher accuracy, lesser error rate and significant AUC and GM for inter-project and inter-version evaluations. (C) 2019 Elsevier B.V. All rights reserved.