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Software defects prediction at the initial period of the software development life cycle remains a critical and important assignment. Defect prediction and correctness leads to the assurance of the quality of software systems and has remained integral to study in the previous years. The quick forecast of imperfect or defective modules in software development can serve the development squad to use the existing assets competently and effectively to provide remarkable software products in a given short timeline. Hitherto, several researchers have industrialized defect prediction models by utilizing statistical and machine learning techniques that are operative and effective approaches to pinpoint the defective modules. Tree family machine learning techniques are well‐thought‐out to be one of the finest and ordinarily used supervised learning methods. In this study, different tree family machine learning techniques are …