Evolutionary sampling and software quality modeling of high-assurance systems
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Improving software-quality predictions with data sampling and boosting
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Review: Software fault prediction: A literature review and current trends
Expert Systems with Applications: An International Journal
An iterative semi-supervised approach to software fault prediction
Proceedings of the 7th International Conference on Predictive Models in Software Engineering
Software defect prediction using semi-supervised learning with dimension reduction
Proceedings of the 27th IEEE/ACM International Conference on Automated Software Engineering
Software mining and fault prediction
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
An in-depth study of the potentially confounding effect of class size in fault prediction
ACM Transactions on Software Engineering and Methodology (TOSEM)
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Software quality assurance is a vital component of software project development. A software quality estimation model is trained using software measurement and defect (software quality) data of a previously developed release or similar project. Such an approach assumes that the development organization has experience with systems similar to the current project and that defect data are available for all modules in the training data. In software engineering practice, however, various practical issues limit the availability of defect data for modules in the training data. In addition, the organization may not have experience developing a similar system. In such cases, the task of software quality estimation or labeling modules as fault prone or not fault prone falls on the expert. We propose a semisupervised clustering scheme for software quality analysis of program modules with no defect data or quality-based class labels. It is a constraint-based semisupervised clustering scheme that uses k-means as the underlying clustering algorithm. Software measurement data sets obtained from multiple National Aeronautics and Space Administration software projects are used in our empirical investigation. The proposed technique is shown to aid the expert in making better estimations as compared to predictions made when the expert labels the clusters formed by an unsupervised learning algorithm. In addition, the software quality knowledge learnt during the semisupervised process provided good generalization performance for multiple test data sets. An analysis of program modules that remain unlabeled subsequent to our semisupervised clustering scheme provided useful insight into the characteristics of their software attributes