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.

All engineering disciplines are founded and rely on models, although they may differ on purposes and usages of modeling. Interdisciplinary domains such as Cyber Physical Systems (CPSs) seek approaches that incorporate different modeling needs and usages. Specifically, the Simulink modeling platform greatly appeals to CPS engineers due to its seamless support for simulation and code generation. In this paper, we propose a test generation approach that is applicable to Simulink models built for both purposes of simulation and code generation. We define test inputs and outputs as signals that capture evolution of values over time. Our test generation approach is implemented as a meta-heuristic search algorithm and is guided to produce test outputs with diverse shapes according to our proposed notion of diversity. Our evaluation, performed on industrial and public domain models, demonstrates that: (1) In contrast to the existing tools for testing Simulink models that are only applicable to a subset of code generation models, our approach is applicable to both code generation and simulation Simulink models. (2) Our new notion of diversity for output signals outperforms random baseline testing and an existing notion of signal diversity in revealing faults in Simulink models. (3) The fault revealing ability of our test generation approach outperforms that of the Simulink Design Verifier, the only testing toolbox for Simulink.

As Simulink is a widely used language in the embedded industry, there is a growing need to support debugging activities for Simulink models. In this work, we propose an approach to localize multiple faults in Simulink models. Our approach builds on statistical debugging and is iterative. At each iteration, we identify and resolve one fault and re-test models to focus on localizing faults that might have been masked before. We use decision trees to cluster together failures that satisfy similar (logical) conditions on model blocks or inputs. We then present two alternative selection criteria to choose a cluster that is more likely to yield the best fault localization results among the clusters produced by our decision trees. Engineers are expected to inspect the ranked list obtained from the selected cluster to identify faults. We evaluate our approach on 240 multi-fault models obtained from three different industrial subjects. We compare our approach with two baselines: (1) Statistical debugging without clustering, and (2) State-of-the-art clustering-based statistical debugging. Our results show that our approach significantly reduces the number of blocks that engineers need to inspect in order to localize all faults, when compared with the two baselines. Furthermore, with our approach, there is less performance degradation than in the baselines when increasing the number of faults in the underlying models.

Summary Debugging Simulink models presents a significant challenge in the embedded industry. This paper proposesSimFL, a fault localization approach for Simulink models by combining statistical debugging and dynamic model slicing. Simulink models, being visual and hierarchical, have multiple outputs at different hierarchy levels. Given a set of outputs to observe for localizing faults, we generate test execution slices, for each test case and output, of the Simulink model. In order to further improve fault localization accuracy, we proposeiSimFL, an iterative fault localization algorithm. At each iteration,iSimFLincreases the set of observable outputs by including outputs at lower hierarchy levels, thus increasing the test oracle cost but offsetting it with significantly more precise fault localization. We utilize a heuristic stopping criterion to avoid unnecessary test oracle extension. We evaluate our work on three industrial Simulink models from Delphi Automotive. Our results show that, on average,SimFLranks faulty blocks in the top 8.9% in the list of suspicious blocks. Further, we show thatiSimFLsignificantly improves this percentage down to 4.4% by requiring engineers to observe only an average of five additional outputs at lower hierarchy levels on top of high‐level model outputs. Copyright © 2016 John Wiley & Sons, Ltd.

Modeling mixed discrete-continuous controllers using Stateflow is common practice and has a long tradition in the embedded software system industry. Testing Stateflow models is complicated by expensive and manual test oracles that are not amenable to full automation due to the complex continuous behaviors of such models. In this paper, we reduce the cost of manual test oracles by providing test case selection algorithms that help engineers develop small test suites with high fault revealing power for Stateflow models. We present six test selection algorithms for discrete-continuous Stateflows: An adaptive random test selection algorithm that diversifies test inputs, two white-box coverage-based algorithms, a black-box algorithm that diversifies test outputs, and two search-based black-box algorithms that aim to maximize the likelihood of presence of continuous output failure patterns. We evaluate and compare our test selection algorithms, and find that our three output-based algorithms consistently outperform the coverage- and input-based algorithms in revealing faults in discrete-continuous Stateflow models. Further, we show that our output-based algorithms are complementary as the two search-based algorithms perform best in revealing specific failures with small test suites, while the output diversity algorithm is able to identify different failure types better than other algorithms when test suites are above a certain size.

Continuous controllers have been widely used in automotive domain to monitor and control physical components. These controllers are subject to three rounds of testing: Model-in-the-Loop (MiL), Software-in-the-Loop and Hardware-in-the-Loop. In our earlier work, we used meta-heuristic search to automate MiL testing of fixed configurations of continuous controllers. In this paper, we extend our work to support MiL testing of all feasible configurations of continuous controllers. Specifically, we use a combination of dimensionality reduction and surrogate modeling techniques to scale our earlier MiL testing approach to large, multi-dimensional input spaces formed by configuration parameters. We evaluated our approach by applying it to a complex, industrial continuous controller. Our experiment shows that our approach identifies test cases indicating requirements violations. Further, we demonstrate that dimensionally reduction helps generate surrogate models with higher prediction accuracy. Finally, we show that combining our search algorithm with surrogate modelling improves its efficiency for two out of three requirements.

We present CoCoTest, a tool for automated testing of continuous controllers at the Model-in-the-Loop stage. CoCoTest combines explorative and exploitative search algorithms to identify scenarios in the controller input space that violate or are likely to violate the controller requirements. This enables a scalable and systematic way to test continuous properties of such controllers. Our experiments show that CoCoTest identifies critical flaws in the controller design that are rarely found by manual testing and go unnoticed until late stages of embedded software system development.