Hierarchical Behavior Annex: Towards an AADL Functional Specification Extension

Xu, Jianhua; Yang, Zhibin; Huang, Zhiqiu; Zhou, Yong; Li, Chengwei; Li, Xue; Bodeveix, Jean-Paul; Filali, Mamoun

doi:10.1109/memcod.2018.8557005

Cited by 2 publications

(1 citation statement)

References 13 publications

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“…1) a requirement modeling method based on restricted Chinese natural language for Aerospace Software. This requirement modeling method can be extended to other safety-critical domains, such as railway and automotive; 2) automatically generate the AADL models from the RM-RNL through model transformations; 3) refinement of the initial AADL models with graphical BA/hierarchical BA [24] and graphical synchronous language SIGNAL [25]; 4) provide an integrated verification environment for AADL models using existing verification and analysis tools, such as Timed Abstract State Machine (TASM) [26], UPPAAL [27], and so on. MACAerospace can perform compositional verification based on AGREE [28] at system level.…”

Section: A Macaerospace Projectmentioning

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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