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Software testing is a crucial task during software development process with the potential to save time and budget by recognizing defects as early as possible and delivering a more defect-free product. To improve the testing process, fault prediction approaches identify parts of the system that are more defect prone. However, when the defect data or quality-based class labels are not identified or the company does not have similar or earlier versions of the software project, researchers cannot use supervised classification methods for defect detection. In order to detect defect proneness of modules in software projects with high accuracy and improve detection model generalization ability, we propose an automated software fault detection model using semi-supervised hybrid self-organizing map (HySOM). HySOM is a semi-supervised model based on self-organizing map and artificial neural network. The advantage of …