A model for feature selection in software fault prediction based on memetic algorithm and fuzzy logic

Today, due to high costs, it is not possible to perform a comprehensive and complete test on all parts of thesoftware. But if the fault-prone parts are identified before the test, the main focus of the test can be placedon these parts, which leads to cost savings. Identifying fault-prone components is the main purpose ofsoftware fault prediction. A predictive model receives software modules along with their features as inputand predicts which ones are prone to fault. Machine learning techniques are commonly used to constructthese models, the performance of which is highly dependent on the training dataset. Training datasetsusually have many software features, some of which are irrelevant or redundant, and the removal of thesefeatures is done using feature selection methods. In this research, a new method for wrapper-based featureselection is proposed that uses memetic algorithm, random forest technique and a new criterion based onfuzzy inference system. The results show that the proposed fuzzy evaluation criterion has a betterperformance than the existing criteria and improves the performance of feature selection. The final purposeof this research is to achieve a robust model for predicting high performance software faults and thecomparison results show that the proposed model has higher performance than other models.