  The research on developing software defect prediction (SDP) models is
targeted at reducing the workload on the tester and, thereby, the time spent on
the targeted module. However, while a considerable amount of research has been
done on developing prediction models or attempting to mitigate the related
issues in developing prediction models, it is still unknown whether the
developed prediction model really works in real-world testing environments or
not. With this article, we bridge this research gap of finding the feasibility
of the developed binary defect prediction model in the real-world testing
environments. Because machine learning (ML) applications span over many
domains, we hope this article may provide sufficient ground to do research on
analysing the feasibility of developed prediction models in the related
applications in real-time scenarios.

Software Engineering