Doctoral Thesis / Dissertation from the year 2018 in the subject Computer Science - Software, grade: Excellence, Mälardalen University (The school of Innovation, design and engineering), language: English, abstract: Optimizing the software testing process has received much attention over the last few decades. Test optimization is typically seen as a multi-criteria decision making problem. One aspect of test optimization involves test selection, prioritization and execution scheduling. Having an efficient test process can result in the satisfaction of many objectives such as cost and time minimization. It can also lead to on-time delivery and better quality of the final software product. To achieve the goal of test efficiency, a set of criteria, having an impact on the test cases, needs to be identified. The analysis of several industrial case studies and also state of the art in this thesis, indicate that the dependency between integration test cases is one such criterion, which has a direct impact on test execution results. Other criteria of interest include requirement coverage and test execution time. In this doctoral thesis, we introduce, apply and evaluate a set of approaches and tools for test execution optimization at industrial integration testing level in embedded software development. Furthermore, ESPRET (Estimation and Prediction of Execution Time) and sOrTES (Stochastic Optimizing of Test Case Scheduling) are our proposed supportive tools for predicting the execution time and the scheduling of manual integration test cases, respectively. All proposed methods and tools in this thesis, have been evaluated at industrial testing projects at Bombardier Transportation (BT) in Sweden. As a result of the scientific contributions made in this doctoral thesis, employing the proposed approaches has led to an improvement in terms of reducing redundant test execution failures of up to 40% with respect to the current test execution approach at BT.
Sahar Tahvili is a senior data scientist at Ericsson and also a postdoctoral researcher at the software testing laboratory at Mälardalen University. Sahar's research focuses on artificial intelligence (AI), advanced methods for testing complex software-intensive systems and designing decision support systems (DSS). Sahar holds a Ph.D.in Software engineering from Mälardalen University since 2018. Her doctoral thesis entitled " Multi-Criteria Optimization of System Integration Testing ". In 2014, she graduated as M.Phil. in Applied Mathematics with an emphasis on optimization from Mälardalen University. Sahar also has a background in Aeronautical Engineering.
