🔗 Research paper link: https://dl.acm.org/doi/abs/10.1145/3379247.3379278
Article Title: The Impact of Software Fault Prediction in Real-World Application: An Automated Approach for Software Engineering
Software fault prediction and proneness has long been considered as a critical issue for the tech industry and software professionals. In the traditional techniques, it requires previous experience of faults or a faulty module while detecting the software faults inside an application. An automated software fault recovery models enable the software to significantly predict and recover software faults using machine learning techniques. Such ability of the feature makes the software to run more effectively and reduce the faults, time and cost. In this paper, we proposed a software defect predictive development models using machine learning techniques that can enable the software to continue its projected task. Moreover, we used different prominent evaluation benchmark to evaluate the model's performance such as ten-fold cross-validation techniques, precision, recall, specificity, f 1 measure, and accuracy. This study reports a significant classification performance of 98-100% using SVM on three defect datasets in terms of f1 measure. However, software practitioners and researchers can attain independent understanding from this study while selecting automated task for their intended application.
In this experiment, we have used 3 open source publicly available data from PROMISE Software Engineering Database. These datasets Tim Menzies et al. have been used in their research paper [1]. In another study, Jureczko et al. [2] have been assembled a software fault prediction model to predict the software defects using machine learning algorithms. They have discussed in their paper about 8 projects (PROMISE Repository) data and by taking 19 CK metrics and McCabe metrics for constructed a predictive model. In our study, we have used 22 attributes for building our automated fault predict model. Table 1 shows 22 different attributes from software defect datasets including 21 independent metrics and one is outcome information. i.e. which is faulty and no-fault.
Reference: [1] T. Menzies, J. Distefano, A. O. S, and R. M. Chapman, “Assessing Predictors of Software Defects.” [2] M. Jureczko and L. Madeyski, “Towards identifying software project clusters with regard to defect prediction,” in Proceedings of the 6th International Conference on Predictive Models in Software Engineering - PROMISE ’10, 2010, p. 1.
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