Test Generation and Test Prioritization for Simulink Models with Dynamic Behavior

All engineering disciplines are founded and rely on models, although they may differ on purposes and usages of modeling. Among the different disciplines, the engineering of Cyber Physical Systems (CPSs) particularly relies on models with dynamic behaviors (i.e., models that exhibit time-varying changes). The Simulink modeling platform greatly appeals to CPS engineers since it captures dynamic behavior models. It further provides seamless support for two indispensable engineering activities: (1) automated verification of abstract system models via model simulation, and (2) automated generation of system implementation via code generation. We identify three main challenges in the verification and testing of Simulink models with dynamic behavior, namely incompatibility, oracle and scalability challenges. We propose a Simulink testing approach that attempts to address these challenges. Specifically, we propose a black-box test generation approach, implemented based on meta-heuristic search, that aims to maximize diversity in test output signals generated by Simulink models. We argue that in the CPS domain test oracles are likely to be manual and therefore the main cost driver of testing. In order to lower the cost of manual test oracles, we propose a test prioritization algorithm to automatically rank test cases generated by our test generation algorithm according to their likelihood to reveal a fault. Engineers can then select, according to their test budget, a subset of the most highly ranked test cases. To demonstrate scalability, we evaluate our testing approach using industrial Simulink models. Our evaluation shows that our test generation and test prioritization approaches outperform baseline techniques that rely on random testing and structural coverage

Evaluating Model Testing and Model Checking for Finding Requirements Violations in Simulink Models

Matlab/Simulink is a development and simulation language that is widely used by the Cyber-Physical System (CPS) industry to model dynamical systems. There are two mainstream approaches to verify CPS Simulink models: model testing that attempts to identify failures in models by executing them for a number of sampled test inputs, and model checking that attempts to exhaustively check the correctness of models against some given formal properties. In this paper, we present an industrial Simulink model benchmark, provide a categorization of different model types in the benchmark, describe the recurring logical patterns in the model requirements, and discuss the results of applying model checking and model testing approaches to identify requirements violations in the benchmarked models. Based on the results, we discuss the strengths and weaknesses of model testing and model checking. Our results further suggest that model checking and model testing are complementary and by combining them, we can significantly enhance the capabilities of each of these approaches individually. We conclude by providing guidelines as to how the two approaches can be best applied together.Comment: 10 pages + 2 page reference

AUTOMATED TESTING OF SIMULINK/STATEFLOW MODELS IN THE AUTOMOTIVE DOMAIN

Context. Simulink/Stateflow is an advanced system modeling platform which is prevalently used in the Cyber Physical Systems domain, e.g., automotive industry, to implement software con- trollers. Testing Simulink models is complex and poses several challenges to research and prac- tice. Simulink models often have mixed discrete-continuous behaviors and their correct behav- ior crucially depends on time. Inputs and outputs of Simulink models are signals, i.e., values evolving over time, rather than discrete values. Further, Simulink models are required to operate satisfactory for a large variety of hardware configurations. Finally, developing test oracles for Simulink models is challenging, particularly for requirements capturing their continuous aspects. In this dissertation, we focus on testing mixed discrete-continuous aspects of Simulink models, an important, yet not well-studied, problem. The existing Simulink testing techniques are more amenable to testing and verification of logical and state-based properties. Further, they are mostly incompatible with Simulink models containing time-continuos blocks, and floating point and non- linear computations. In addition, they often rely on the presence of formal specifications, which are expensive and rare in practice, to automate test oracles. Approach. In this dissertation, we propose a set of approaches based on meta-heuristic search and machine learning techniques to automate testing of software controllers implemented in Simulink. The work presented in this dissertation is motived by Simulink testing needs at Delphi Automotive Systems, a world leading part supplier to the automotive industry. To address the above-mentioned challenges, we rely on discrete-continuous output signals of Simulink models and provide output- based black-box test generation techniques to produce test cases with high fault-revealing ability. Our algorithms are black-box, hence, compatible with Simulink/Stateflow models in their en- tirety. Further, we do not rely on the presence of formal specifications to automate test oracles. Specifically, we propose two sets of test generation algorithms for closed-loop and open-loop con- trollers implemented in Simulink: (1) For closed-loop controllers, test oracles can be formalized and automated relying on the feedback received from the controlled system. We characterize the desired behavior of closed-loop controllers in a set of common requirements, and then use search to identify the worst-case test scenarios of the controller with respect to each requirement. (2) For open-loop controllers, we cannot automate test oracles since the feedback is not available, and test oracles are manual. Hence, we focus on providing test generation algorithms that develop small effective test suites with high fault revealing ability. We further provide a test case prioriti- zation algorithm to rank the generated test cases based on their fault revealing ability and lower the manual oracle cost. Our test generation and prioritization algorithms are evaluated with several industrial and publicly available Simulink models. Specifically, we showed that fault revealing ability of our our approach outperforms that of Simulink Design Verifier (SLDV), the only test generation toolbox of Simulink and a well-known commercial Simulink testing tool. In addition, using our approach, we were able to detect several real faults in Simulink models from our industry partner, Delphi, which had not been previously found by manual testing based on domain expertise and existing Simulink testing tools. Contributions. The main research contributions in this dissertation are: 1. An automated approach for testing closed-loop controllers that characterize the desired be- havior of such controllers in a set of common requirements, and combines random explo- ration and search to effectively identify the worst-case test scenarios of the controller with respect to each requirement. 2. An automated approach for testing highly configurable closed-loop controllers by account- ing for all their feasible configurations and providing strategies to scale the search to large multi-dimensional spaces relying on dimensionality reduction and surrogate modelling 3. A black-box output-based test generation algorithm for open-loop Simulink models which uses search to maximize the likelihood of presence of specific failure patterns (i.e., anti- patterns) in Simulink output signals. 4. A black-box output-based test generation algorithm for open-loop Simulink models that maximizes output diversity to develop small test suites with diverse output signal shapes and, hence, high fault revealing ability. 5. A test case prioritization algorithm which relies on output diversity of the generated test suites, in addition to the dynamic structural coverage achieved by individual tests, to rank test cases and help engineers identify faults faster by inspecting a few test cases. 6. Two test generation tools, namely CoCoTest and SimCoTest, that respectively implement our test generation approaches for closed-loop and open-loop controllers