Software Defect Prediction using Supervised Machine Learning: A Systematic Literature Review
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Abstract
Software testing is the process of finding faults in software while executing it. The results of the testing are used to find and correct faults. Software defect prediction estimates is where faults are likely to occur in source code. The results from the defect prediction can be used to optimize testing and ultimately improve software quality. Machine learning, that concerns computer programs learning from data, is used to build prediction models which then can be used to classify data. Many researchers have already been working in the field of defect prediction in software using some machine learning algorithms. Their results vary from dataset to dataset. These algorithms give inconsistent output for predicting defects in a random software project. Researchers have not decided which machine learning algorithm is best suitable for correctly predicting the defects in software so recent developments in machine learning introduce ensembling methods to predict defects. Ensembling takes the advantages of different techniques to give a better prediction of defects compared to individual base models. In this paper the study of different machine learning algorithm for software defect prediction.
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