Test-case generation for embedded simulink via formal concept analysis

He, Nannan; Rümmer, Philipp; Kroening, Daniel

doi:10.1145/2024724.2024777

Cited by 50 publications

(37 citation statements)

References 16 publications

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“…The notion of Simulink model mutations have been addressed previously by Zhan and Clark [18], He et al [19], and Araujo et al [20]. In these examples, they describe mutations that explicitly try to mutate a model's run-time properties.…”

Section: Discussionmentioning

confidence: 99%

Towards a Taxonomy for Simulink Model Mutations

Stephan

Alalfi

Cordy

2014

2014 IEEE Seventh International Conference on Software Testing, Verification and Validation Workshops

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Abstract-A relatively new and important branch of Mutation Analysis involves model mutations. In our attempts to realize model-clone detector testing, we found that there was little mutation research on Simulink, which is a fairly prevalent modeling language, especially in embedded domains. Because Simulink model mutations are the crux of our model-clone detector testing framework, we want to ensure that we are selecting the appropriate mutations. In this paper, we propose a taxonomy of Simulink model mutations, which is based on our experiences thus far with Simulink, that aims to inject model clones of various types and is fairly representative of realistic Simulink edit operations. We organize the mutations by categories based on the types of model clones they will inject, and further break them down into mutation classes. For each class, we define the characteristics of mutation operators belonging to that class and demonstrate an example operator. Lastly, in an attempt to validate our taxonomy, we perform a case study on multiple versions of three Simulink projects, including an industrial project, to ascertain if the actual subsystem edit operations observed across versions can be classified using our taxonomy and present any interesting cases. While we developed the taxonomy with the specific goal of facilitating and guiding the injection of mutants for model clones, we believe it is fairly general and a solid foundation for future Simulink model mutation work.

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Section: Discussionmentioning

confidence: 99%

Towards a Taxonomy for Simulink Model Mutations

Stephan

Alalfi

Cordy

2014

2014 IEEE Seventh International Conference on Software Testing, Verification and Validation Workshops

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“…Other Simulink model mutation frameworks have been proposed by Zhan and Clark [9] and He et al [10]. Unlike our framework, their motivation is testing and fault analysis of concrete Simulink models, and ensuring sufficient test-case coverage for the generated mutants.…”

Section: Related Workmentioning

confidence: 99%

Using mutation analysis for a model-clone detector comparison framework

Stephan

Alafi

Stevenson

et al. 2013

2013 35th International Conference on Software Engineering (ICSE)

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Abstract-Model-clone detection is a relatively new area and there are a number of different approaches in the literature. As the area continues to mature, it becomes necessary to evaluate these approaches against each other and validate new ones that are introduced. We present a mutation-analysis based model-clone detection framework that attempts to automate and standardize the process of comparing multiple Simulink modelclone detection tools or variations of the same tool. By having such a framework, new research directions in the area of modelclone detection can be facilitated as the framework can be used to validate new techniques as they arise. We begin by presenting challenges unique to model-clone tool comparison including recall calculation, the nature of the clones, and the clone report representation. We propose our framework, which we believe will address these challenges. This is followed by a presentation of the mutation operators that we plan to inject into our Simulink models that will introduce variations of all the different model clone types that can then be searched for by each respective model-clone detector.

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“…State of the art formal methods tools, including static analysis, theorem proving, and model checking, are insufficient in tackling the challenges in CPS verification and validation [32]. Other verification techniques, including model-based testing [11] and simulation [16] have high learning curves, impractical development costs, and scalability issues. Domain specific tools (e.g., passive distributed assertions [29] and symbolic execution [30]), though more scalable, fail to formally verify either qualitative constraints (e.g., the ordering of events), quantitative ones (e.g., timing), or both.…”

Section: Introductionmentioning

confidence: 99%

BraceAssertion: Runtime Verification of Cyber-Physical Systems

Zheng

Julien

Podorozhny

et al. 2015

2015 IEEE 12th International Conference on Mobile Ad Hoc and Sensor Systems

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Abstract-Cyber-Physical Systems (CPS) have gained wide popularity, however, developing and debugging CPS remain significant challenges. Many bugs are detectable only at runtime under deployment conditions that may be unpredictable or at least unexpected at development time. The current state of the practice of debugging CPS is generally ad hoc, involving trial and error in a real deployment. For increased rigor, it is appealing to bring formal methods to CPS verification. However developers often eschew formal approaches due to complexity and lack of efficiency. This paper presents BraceAssertion, a specification framework based on natural language queries that are automatically converted to a determinitic class of timed automata used for runtime monitoring. To reduce runtime overhead and support properties that reference predicate logic, we use a second monitor automaton to create filtered traces on which to run the analysis using the specification monitor. We evaluate the BraceAssertion framework using a real CPS case study and show that the framework is able to minimize runtime overhead with an increasing number of monitors. I. INTRODUCTIONCyber-Physical Systems (CPS) are found in applications in structural monitoring, autonomous vehicles, and many other fields. Compared with the growth of the domain, verification and validation of CPS lags far behind [32]. The state of the practice in debugging CPS generally entails a combination of simulation and in situ debugging. In a 2007 DARPA Urban Challenge Vehicle, a bug undetected by more than 300 miles of test-driving resulted in a near collision. An analysis of the incident found that, to protect the steering system, the interface to the physical hardware limited the steering rate to low speeds [23]. When the path planner produced a sharp turn at higher speeds, the vehicle physically could not follow, and this unanticipated situation caused the bug. The analysis concluded that, although simulation-centric tools are indispensable for rapid prototyping, design, and debugging, they are limited in providing correctness guarantees. State of the art formal methods tools, including static analysis, theorem proving, and model checking, are insufficient in tackling the challenges in CPS verification and validation [32]. Other verification techniques, including model-based testing [11] and simulation [16] have high learning curves, impractical development costs, and scalability issues. Domain specific tools (e.g., passive distributed assertions [29] and symbolic execution [30]), though more scalable, fail to formally verify either qualitative constraints (e.g., the ordering of events), quantitative ones (e.g., timing), or both. Ad hoc debugging has become the de facto

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Test-case generation for embedded simulink via formal concept analysis

Cited by 50 publications

References 16 publications

Towards a Taxonomy for Simulink Model Mutations

Towards a Taxonomy for Simulink Model Mutations

Using mutation analysis for a model-clone detector comparison framework

BraceAssertion: Runtime Verification of Cyber-Physical Systems

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