Machine Learning Based Computational Intelligence in Software Engineering

Abstract

For the goal of software engineering optimization, data analytics and statistical modeling make use of methodologies that are predicated on metrics, patterns, and anomalies. In order to satisfy these objectives, it is necessary to compile a list of organizational security requirements and to design appropriate controls. Before giving the finished product to the customer, its quality can be made sure by finding and fixing any flaws or mistakes that are found during the testing phase. In this paper, an ensemble learning based computational intelligence developed to identify the faults in the software. The simulation is conducted on the software datasets to test the efficacy of the model. The results of simulation show that the proposed method achieves higher classification accuracy than other methods.