Detecting safety‐related components in statecharts through traceability and model slicing

Kan, Shuanglong; Huang, Zhiqiu

doi:10.1002/spe.2526

Cited by 4 publications

(2 citation statements)

References 29 publications

(38 reference statements)

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“…Several other approaches combine selected safety aspects with MDD [31][32][33][34]. However, they target other phases of the development lifecycle rather than the actual realization step of the system which is the focus of our approach.…”

Section: Related Workmentioning

confidence: 99%

Automatic Code Generation of Safety Mechanisms in Model-Driven Development

Huning¹,

Pulvermueller²

2021

Electronics

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In order to meet regulatory standards in the domain of safety-critical systems, these systems have to include a set of safety mechanisms depending on the Safety Integrity Level (SIL). This article proposes an approach for how such safety mechanisms may be generated automatically via Model-Driven Development (MDD), thereby improving developer productivity and decreasing the number of bugs that occur during manual implementation. The approach provides a structured way to define safety requirements, which may be parsed automatically and are used for the generation of software-implemented safety mechanisms, as well as the initial configuration of hardware-implemented safety mechanisms. The approach for software-implemented safety mechanisms relies on the Unified Modeling Language (UML) for representing these mechanisms in the model and uses model transformations to realize them in an intermediate model, from which code may be generated with simple 1:1 mappings. The approach for hardware-implemented safety mechanisms builds upon a template-based code snippet repository and a graphical user interface for configuration. The approach is applied to the development of a safety-critical fire detection application and the runtime of the model transformations is evaluated, indicating a linear scalability of the transformation steps. Furthermore, we evaluate the runtime and memory overhead of the generated code.

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Section: Related Workmentioning

confidence: 99%

Automatic Code Generation of Safety Mechanisms in Model-Driven Development

Huning¹,

Pulvermueller²

2021

Electronics

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“…Traceability links provide a critical support for numerous software engineering activities, including safety analysis, compliance verification, and impact prediction [16]. Therefore, traceability is a critical element of almost all SCS development processes.…”

Section: A Research Questionsmentioning

confidence: 99%

An Approach to Generate the Traceability Between Restricted Natural Language Requirements and AADL Models

Wang¹,

Yang²,

Huang³

et al. 2020

IEEE Trans. Rel.

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Requirements traceability is broadly recognized as a critical element of any rigorous software development process, especially for building safety-critical software (SCS) systems. Modeldriven development (MDD) is increasingly used to develop SCS in many domains, such as automotive and aerospace. MDD provides new opportunities for establishing traceability links through modeling and model transformations. Architecture Analysis and Design Language (AADL) is a standardized architecture description language for embedded systems, which is widely used in avionics and aerospace industries to model safety-critical applications. However, there is a big challenge to automatically establish the traceability links between requirements and AADL models in the context of MDD, because requirements are mostly written as free natural language texts, which are often ambiguous and difficult to be processed automatically. To bridge the gap between natural language requirements (NLRs) and AADL models, we propose an approach to generate the traceability links between NLRs and AADL models. First, we propose a requirement modeling method based on the restricted natural language, which is named as RM-RNL. The RM-RNL can eliminate the ambiguity of NLRs and barely change engineers' habits of requirement specification. Second, we present a method to automatically generate the initial AADL models from the RM-RNLs and to automatically establish traceability links between the elements of the RM-RNL and the generated AADL models. Third, we refine the initial AADL models through patterns to achieve the change of requirements and traceability links. Finally, we demonstrate the effectiveness of our approach with industrial case studies and evaluation experiments.

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