Traditional software testing usually comes with manual definitions of test cases. This manual process can be time-consuming, tedious, and incomplete in covering important but elusive corner cases that are hardly identifiable. Automatic generation of random test cases emerges as a strategy to mitigate the challenges associated with the manual test case design. However, the effectiveness of random test cases in fault detection may be limited, leading to increased testing costs, particularly in systems where test execution demands substantial resources and time. Leveraging the domain knowledge of test experts can guide the automatic random generation of test cases to more effective zones. In this thesis, we target quality assurance of multiagent autonomous systems and aim to automate test generation for them by applying the domain knowledge of test experts.

To formalize the specification of the domain expert's knowledge, we introduce a small Domain Specific Language (DSL) for formal specification of particular locality-based constraints for grid-based multiagent systems. We initially employ this DSL for filtering randomly generated test inputs. Then, we evaluate the effectiveness of the generated test cases through an experiment on a case study of autonomous agents. Applying statistical analysis on the experiment results demonstrates that utilizing the domain knowledge to specify test selection criteria for filtering randomly generated test cases significantly reduces the number of potentially costly test executions to identify the persisting faults. 

Domain knowledge of experts can also be utilized to directly generate test inputs with constraint solvers. We conduct a comprehensive study to compare the performance of filtering random cases and constraint-solving approaches in generating selective test cases across various test scenario parameters. The examination of these parameters provides criteria for determining the suitability of random data filtering versus constraint solving, considering the varying size and complexity of the test input generation constraint. To conduct our experiments, we use QuickCheck tool for random test data generation with filtering, and we employ Z3 for constraint solving. The findings, supported by observations and statistical analysis, reveal that test scenario parameters impact the performance of filtering and constraint-solving approaches differently. Specifically, the results indicate complementary strengths between the two approaches: random generation and filtering approach excels for the systems with a large number of agents and long agent paths but shows degradation in larger grid sizes and stricter constraints. Conversely, constraint solving approach demonstrates robust performance for large grid sizes and strict constraints but experiences degradation with increased agent numbers and longer paths.

Our initially proposed DSL is limited in its features and is only capable of specifying particular locality-based constraints. To be able to specify more elaborate test scenarios, we extend that DSL based on a more intricate model of autonomous agents and their environment. Using the extended DSL, we can specify test oracles and test scenarios for a dynamic grid environment and agents having several attributes. To assess the extended DSL's utility, we design a questionnaire to gather opinions from several experts and also run an experiment to compare the efficiency of the extended DSL with the initially proposed one. The questionnaire results indicate that the extended DSL was successful in specifying several scenarios that the experts found more useful than the scenarios specified by the initial DSL. Moreover, the experimental results demonstrate that testing with the extended DSL can significantly reduce the number of test executions to detect system faults, leading to a more efficient testing process.