首页|Qassim University Researcher Adds New Findings in the Area of Machine Learning ( Software Defect Prediction Based on Machine Learning and Deep Learning Technique s: An Empirical Approach)
Qassim University Researcher Adds New Findings in the Area of Machine Learning ( Software Defect Prediction Based on Machine Learning and Deep Learning Technique s: An Empirical Approach)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Research findings on artificial intell igence are discussed in a new report. According to news reporting originating fr om Buraydah, Saudi Arabia, by NewsRx correspondents, research stated, “Software bug prediction is a software maintenance technique used to predict the occurrenc es of bugs in the early stages of the software development process.” Our news editors obtained a quote from the research from Qassim University: “Ear ly prediction of bugs can reduce the overall cost of software and increase its r eliability. Machine learning approaches have recently offered several prediction methods to improve software quality. This paper empirically investigates eight well-known machine learning and deep learning algorithms for software bug predic tion. We compare the created models using different evaluation metrics and a wel l-accepted dataset to make the study results more reliable. This study uses a la rge dataset collected from five publicly available bug datasets that includes ab out 60 software metrics. The source-code metrics of internal class quality, incl uding cohesion, coupling, complexity, documentation inheritance, and size metric s, were used as features to predict buggy and non-buggy classes.”
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