Toward Learning Realizable Scenario-Based, Formal Requirements Specifications

Schmelter, David; Greenyer, Joel; Holtmann, Jörg

doi:10.1109/rew.2017.14

Cited by 2 publications

(2 citation statements)

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“…Search-based software engineering [52][53][54] is a well-established software engineering field that applies meta-heuristic algorithms [39] to automatically solve such search problems. Similar to our previous work on automatically completing underspecified scenario models [110], the problem of finding real-time-feasible platform-specific MSD specifications could be encoded as input to a metaheuristic algorithm that has to incorporate the timing analysis results.…”

Section: Discussionmentioning

confidence: 99%

Early timing analysis based on scenario requirements and platform models

2022

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Distributed, software-intensive systems (e.g., in the automotive sector) must fulfill communication requirements under hard real-time constraints. The requirements have to be documented and validated carefully using a systematic requirements engineering (RE) approach, for example, by applying scenario-based requirements notations. The resources of the execution platforms and their properties (e.g., CPU frequency or bus throughput) induce effects on the timing behavior, which may lead to violations of the real-time requirements. Nowadays, the platform properties and their induced timing effects are verified against the real-time requirements by means of timing analysis techniques mostly implemented in commercial-off-the-shelf tools. However, such timing analyses are conducted in late development phases since they rely on artifacts produced during these phases (e.g., the platform-specific code). In order to enable early timing analyses already during RE, we extend a scenario-based requirements notation with allocation means to platform models and define operational semantics for the purpose of simulation-based, platform-aware timing analyses. We illustrate and evaluate the approach with an automotive software-intensive system.

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Section: Discussionmentioning

confidence: 99%

Early timing analysis based on scenario requirements and platform models

2022

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“…In contrast, we do not make any assumption on how the environment chooses its strategy. Finally, in the context of specification-repair in zerosum games multiple automated methods for repairing environment models exist, e.g., [22,15,16,20,8]. Unfortunately, all of these methods fail to provide permissiveness.…”

Section: Introductionmentioning

confidence: 99%

Computing Adequately Permissive Assumptions for Synthesis

Anand

Mallik

Nayak

et al. 2023

Lecture Notes in Computer Science

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We automatically compute a new class of environment assumptions in two-player turn-based finite graph games which characterize an “adequate cooperation” needed from the environment to allow the system player to win. Given an $$\omega $$ ω -regular winning condition $$\varPhi $$ Φ for the system player, we compute an $$\omega $$ ω -regular assumption $$\varPsi $$ Ψ for the environment player, such that (i) every environment strategy compliant with $$\varPsi $$ Ψ allows the system to fulfill $$\varPhi $$ Φ (sufficiency), (ii) $$\varPsi $$ Ψ can be fulfilled by the environment for every strategy of the system (implementability), and (iii) $$\varPsi $$ Ψ does not prevent any cooperative strategy choice (permissiveness).For parity games, which are canonical representations of $$\omega $$ ω -regular games, we present a polynomial-time algorithm for the symbolic computation of adequately permissive assumptions and show that our algorithm runs faster and produces better assumptions than existing approaches—both theoretically and empirically. To the best of our knowledge, for $$\omega $$ ω -regular games, we provide the first algorithm to compute sufficient and implementable environment assumptions that are also permissive.

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Toward Learning Realizable Scenario-Based, Formal Requirements Specifications

Cited by 2 publications

References 10 publications

Early timing analysis based on scenario requirements and platform models

Early timing analysis based on scenario requirements and platform models

Computing Adequately Permissive Assumptions for Synthesis

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