A Proposed Method of Machine Learning based Framework for Software Product Line Testing
Ashish Saini, Raj Kumar, Amrita Kumari, Satender Kumar
Abstract
Software product line includes a series of software products which share common feature’s set. Since the number of features may grow exponentially, it is not possible to test individual products of the entire product line. Since the time budget for testing is limited or even unknown a priori, the sequence of testing products is critical for effective product line testing. Regression testing is the way to test a product after making some changes in the product (for example, after a new version or product is developed). Due to the lack of resources, only a a subset of test cases is executed for testing a specific product. This leads to problems with important test cases regarding testing. Therefore, to lead the test cases, minimization and prioritization of test cases is initiated by the regression testing technique. Existing techniques usually require source code which is time-consuming and complex to execute. However, testing of complex applications often restricts access to source code. Therefore, complex applications can be tested by black-box testing. In this paper, a machine learning- based technique has been proposed to test the software product line. Fuzzy C-Means clustering has been applied to minimize the test cases and Ranked Support Vector Machine to prioritize the rest of the test cases.