There are several problem areas that must be addressed when applying randomization to unit testing. As yet no general, fully automated solution that works for all units has been proposed. We therefore have developed RUTE-J, a Java package intended to help programmers do randomized unit testing in Java. In this paper, we describe RUTE-J and illustrate how it supports the development of per-unit solutions for the problems of randomized unit testing. We report on an experiment in which we applied RUTE-J to the standard Java TreeMap class, measuring the efficiency and effectiveness of the technique. We also illustrate the use of randomized testing in experimentation, by adapting RUTE-J so that it generates randomized minimal covering test suites, and measuring the effectiveness of the test suites generated.
Basic equation for one-photon absorption 2) Basic equations for the first hyperpolarizability 3) References involved in the above two sections 4) Results of generalized few-state model for more two-photon active systems 5) Orbitals involved in OPA in different systems
Predicting the number of defects in a project is critical for project test managers to allocate budget, resources, and schedule for testing, support and maintenance efforts. Software Defect Prediction models predict the number of defects in given projects after training the model with historical defect related information. The majority of defect prediction studies focused on predicting defect-prone modules from methods, and class-level static information, whereas this study predicts defects from project-level information based on a cross-company project dataset. This study utilizes software sizing metrics, effort metrics, and defect density information, and focuses on developing defect prediction models that apply various machine learning algorithms. One notable issue in existing defect prediction studies is the lack of transparency in the developed models. Consequently, the explain-ability of the developed model has been demonstrated using the state-of-the-art post-hoc model-agnostic method called Shapley Additive exPlanations (SHAP). Finally, important features for predicting defects from cross-company project information were identified.
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