Software fault prediction with data mining techniques by using feature selection based models

Abstract

Software engineering activities comprise of several activities to ensure that the quality product will be achieved at the end. Some of these activities are software testing, inspection, formal verification and software defect prediction. Many researchers have been developed several models for defect prediction. These models are based on machine learning techniques and statistical analysis techniques. The main objective of these models are to identify the defects before the delivery of the software to the end user. This prediction helps project managers to effectively utilize the resources for better quality assurance. Sometimes, a single defect can cause the entire system failure and most of the time they drop the quality of the software system drastically. Early identification of defects can also help to make a better process plan which can handle the defects effectively and increase the customer satisfaction level. But the accurate prediction of defects in software is not an easy task because this is an indirect measure. Therefore, it is important to find suitable and significant measures which are most relevant for finding the defects in the software system. This paper presents a feature selection based model to predict the defects in a given software module. The most relevant features are extracted from all features with the help of seven feature selection techniques and eight classifiers are used to classify the modules. Six NASA software engineering defects prediction data sets are used in this work. Several performance parameters are also calculated for measuring the performance and validation of this work and the results of the experiments revealed that the proposed model has more capability to predict the software defects.