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№2, 2023

SOFTWARE DEFECT PREDICTION USING THE MACHINE LEARNING METHODS

Tamilla A. Bayramova

tamilla@iit.science.az

Reliability of software systems is one of the main indicators of quality. Defects occurring when developing software systems have a direct effect on reliability. Precise prediction of defects in software systems helps software engineers to ensure the reliability of software systems and to properly allocate resources for the trial process. The development of an ensemble method by combining several classification methods occupies one of the main places in research conducted in the field of error prediction in software modules. This paper proposes a method based on the application of ensemble training for defect detection. Here, a database obtained from PROMISE and GITHUB software engineering registry is used to detect defects. Experiments are conducted using Weka software. The prediction efficiency is evaluated based on F-measure and ROC-area. As a result of experiments, the defect detection accuracy of the proposed method is proven to be higher than that of individual machine learning methods (pp.23-31).

Keywords: Random Forest, Naive Bayes, Bagging, Boosting, Ensemble, Software defect prediction
DOI:

http://doi.org/10.25045/jpit.v14.i2.03

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