Scenario testing

Abstract: The present invention relates to a testing tool for testing of complex software systems having a plurality of interrelated system components and a suite of test scenarios for testing the performance of the plurality of interrelated system components. The testing tool includes an Automated Multidimensional Traceability Matrix system for determining linkages between interrelated system components, a means for identifying a change in one or more of the interrelated system components, a means for applying the Automated Multidimensional Traceability Matrix, a means for executing all of or a subset of the test scenarios associated with the interrelated system components that may be affected by the change and a means for evaluating the results of the executed test scenarios.

Abstract: A method, system and program for testing programmatic interfaces by systematically exploring valid call sequences defined by a collection of subroutines with a collection of data. The subject invention does not explicitly write unit tests. Instead, it provides the tools to develop rules which model and verify the correct operation of software while leaving the complexities of test selection, execution, sequencing, coverage, and verification to an automated system. The number of test scenarios generated by the system depends upon cumulative execution time and is reliably independent of human effort.

Abstract: Vision-based control systems are key enablers of many autonomous vehicular systems, including self-driving cars. Testing such systems is complicated by complex and multidimensional input spaces. We propose an automated testing algorithm that builds on learnable evolutionary algorithms. These algorithms rely on machine learning or a combination of machine learning and Darwinian genetic operators to guide the generation of new solutions (test scenarios in our context). Our approach combines multiobjective population-based search algorithms and decision tree classification models to achieve the following goals: First, classification models guide the search-based generation of tests faster towards critical test scenarios (i.e., test scenarios leading to failures). Second, search algorithms refine classification models so that the models can accurately characterize critical regions (i.e., the regions of a test input space that are likely to contain most critical test scenarios). Our evaluation performed on an industrial automotive automotive system shows that: (1) Our algorithm outperforms a baseline evolutionary search algorithm and generates 78% more distinct, critical test scenarios compared to the baseline algorithm. (2) Our algorithm accurately characterizes critical regions of the system under test, thus identifying the conditions that are likely to lead to system failures.