Test case selection and prioritization approach for automated regression testing using ontology and COSMIC measurement

Regression testing is an important activity that aims to provide information about the quality of the software product under test when changes occur. The two primary techniques for optimizing regression testing are test case selection and prioritization. To identify features affected by a change and determine the best test cases for selection and prioritization, techniques allowing the semantic representation and the quantification of testing concepts are required. The goal of this paper is threefold. Firstly, we proposed an ontology-based test case selection model that enables automated regression testing by dynamically selecting appropriate test cases. The selection of test cases is based on a semantic mapping between change requests and their associated test suites and test cases. Secondly, the selected test cases are prioritized based on their functional size. The functional size is determined using the COmmon Software Measurement International Consortium (COSMIC) Functional Size Measurement (FSM) method. The test case prioritization attempts to reorganize test case execution in accordance with its goal. One common goal is fault detection, in which test cases with a higher functional size (i.e., with a higher chance of detecting a fault) are run first, followed by the remaining test cases. Thirdly, we built an automated testing tool using the output of the aforementioned processes to validate the robustness of our proposed research methodology. Results from a case study in the automotive industry domain show that semantically presenting change requests and using standardized FSM methods to quantify their related test cases are the most interesting metrics. Obviously, they assist in the automation of regression testing and, therefore, in all the software testing processes.