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The research on developing software defect prediction (SDP) models istargeted at reducing the workload on the tester and, thereby, the time spent onthe targeted module. However, while a considerable amount of research has beendone on developing prediction models or attempting to mitigate the relatedissues in developing prediction models, it is still unknown whether thedeveloped prediction model really works in real-world testing environments ornot. With this article, we bridge this research gap of finding the feasibilityof the developed binary defect prediction model in the real-world testingenvironments. Because machine learning (ML) applications span over manydomains, we hope this article may provide sufficient ground to do research onanalysing the feasibility of developed prediction models in the relatedapplications in real-time scenarios.

The Probabilistic Bounds on the Feasibility of the Defect Prediction Models in Real-World Testing Environments