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• W2394984143 abstract "Testing in practice is always constrained by limited time and resources available for test execution, and thus, test optimization remains crucial for cost-effective testing. It is even more important when test cases have to be executed manually, e.g., operating physical equipment. Test optimization must take into consideration the complicated tradeoff between cost (e.g., execution time) and effectiveness (e.g., the number of faults caught by a test case). Based on our industrial collaboration within the Maritime domain, we identified a real-world and multiobjective test optimization problem in the context of robustness testing, where test cases require human involvement in certain steps such as turning on the power supply to control module, and manually record observations at certain points. The high-level objective of the thesis was formed to address this industrial challenge: optimizing test cases for execution within limited time budget, where test engineers, depending on the testing requirements, provide weights for various objectives. The System Under Test (SUT) is a high performance, general purpose, real-time process control computer that can be used in a wide variety of system applications in both onand offshore installations. To systematically and precisely understand the SUT, we modeled it using the Unified Modeling Language (UML), and its extension Modeling and Analysis of Real Time and Embedded systems (MARTE), along with the Object Constraint Language (OCL). Similarly, we captured attributes that characterize test cases as a class diagram utilizing international testing standards, e.g., Software Testing Standard (ISO/IEC/IEEE 29119), Risk Management Standard (ISO 31000). These attributes were further used to define various test optimization objectives together with test engineers from our industrial partner. Search based techniques were then used for optimizing test by defining a novel fitness function based on the identified objectives and empirically evaluated it with four search algorithms: Alternating Variable Method (AVM), Genetic Algorithm (GA), (1+1) Evolutionary Algorithm (EA) and Greedy Algorithm. Additionally, we used Random Search as the comparative baseline. We conducted the following three sets of empirical evaluations: 1) Using real data from the industrial problem; 2) Simulating the industrial problem to a larger scale to assess the scalability of the search algorithms; 3) Simulating the industrial problem to a larger scale and assessing the improvement of the performance of the search algorithms along with the increase of the number of iterations. Results show that (1+1) EA performed the best when the number of test" @default.
• W2394984143 created "2016-06-24" @default.
• W2394984143 creator A5034955708 @default.
• W2394984143 date "2015-01-01" @default.
• W2394984143 modified "2023-09-27" @default.
• W2394984143 title "Test Optimization using Weight Based Search Algorithms in a Maritime Application" @default.
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