Linking a state-rich process algebra to a state-free algebra to verify software/hardware implementation

Beg, Arshad; Butterfield, Andrew

doi:10.1145/1943628.1943675

Cited by 7 publications

(5 citation statements)

References 16 publications

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“…The first solution is JCircus [21,36,40] which translates a concrete Circus program to a Java program with JCSP [48]. After that, linking Circus to CSP [7] aims to translate Circus to CSP M then use FDR2 [1] to model-check CSP specification. The last one is mapping Circus processes and refinement to CSP processes and refinement [31,39] that transforms stateful Circus to stateless Circus first by introducing the memory model, and then converts stateless Circus to CSP.…”

Section: Discussionmentioning

confidence: 99%

Model checking of state-rich formalism by linking to $$CSP\,\Vert \,B$$ C S P ‖ B

Woodcock

2015

Int J Softw Tools Technol Transfer

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Since state-rich formalism Circus is a combination of Z, CSP, refinement calculus and Dijkstra's guarded commands, its model checking is intrinsically more complicated and difficult than that of individual state-based languages or process algebras. Current solutions translate executable constructs of Circus programs to Java with JCSP, or translate them to CSP processes. Data aspects of Circus programs are expressed in the Java programming language or as CSP processes. Both of them have disadvantages. This work presents a new approach to model-checking Circus by linking it to CSP B, then we utilise ProB to model-check and animate the CSP B program. The most significant advantage of this approach is the direct mapping of the state part in Circus to Z and finally to B, which maintains the high-level abstraction of data specification. In addition, introduction of deadlock, invariant violation checking, LTL formula checking and animation is another key advantage. We present our approach, a link definition for a subset of Circus constructs, as well as a popular case study (reactive buffer) to show the practical usability of our work. We conclude with a discussion of related work, advantages and potential limitations of our approach, and future work.

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Section: Discussionmentioning

confidence: 99%

Model checking of state-rich formalism by linking to $$CSP\,\Vert \,B$$ C S P ‖ B

Woodcock

2015

Int J Softw Tools Technol Transfer

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“…Our plans for future work include exploring other industrial-scale case studies [3,17,15], as a way of identifying the kind of Circus constructs that would be suitable to have available in our translation tool. We have a particular interest in specifying a translation strategy for Z schemas used as Circus actions within a process.…”

Section: Future Workmentioning

confidence: 99%

Towards a Model-Checker for Circus

Gomes

Butterfield

2019

Lecture Notes in Computer Science

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Among several approaches aiming at the correctness of systems, model-checking is one technique to formally assess system models regarding their desired/undesired behavioural properties. We aim at model-checking the Circus notation that combines Z, CSP, and Morgan's refinement calculus, based on the Unifying Theories of Programming. In this paper, we experiment with approaches for capturing Circus processes in CSP, and for each approach, we evaluate the impact of our decisions on the state-space explored as well as the time spent for such a checking using FDR. We also experimented with the consequences of model-checking CSP models that capture both state invariants and preconditions of Circus models. Some related work on techniques for model-checking Circus was presented by Freitas [12] where a refinement model checker based on automata theory [19] and the operational semantics of Circus [39] was formalised in Z/Eves [34]. He also prototyped a model checker in Java. Moreover, Nogueira et al. [24] also presented a prototype of a model checker based on the operational semantics of Circus within the Microsoft FORMULA [21] framework. However, they could not provide a formal proof of the soundness of their approach, since FORMULA does not have an available formal semantics. Yet another approach for model-checking Circus was defined by Ye and Woodcock [41], who defined a link from Circus to CSP B with model-checking using ProB [31]. Finally Beg [4] prototyped and investigated an automatic translation that supports a subset of Circus constructs. Since CSP M does not have a notion of variables for state as in Z, Circus or even the B-Method, we have to somehow capture them in order to obtain a CSP M model as similar as possible to the original Circus one. Therefore, one could either use a memory model [30,25] in order to manage the values of the state variables, or else, to adopt the idea of state-variable parametrised processes [4]. Following the results presented in ABZ'16 [16], which involved manual translation, we decided to develop Circus2CSP 3 , an automatic translator from Circus into CSP M , aiming at model-checking with FDR. Our tool was then built based on the strategy presented in Section 5.3 of Deliverable 24.1 [29], from the COMPASS project [37], that defines a rigorous but manual translation strategy aiming at obtaining CSP M specifications from Circus. This paper reports design decisions regarding different approaches for model checking and experimental results obtained for Circus specifications. Such experiments were enough to identify an effective general form for any CSP M model derived from Circus, where FDR could perform refinement checks with reduced time and memory consumption compared to existing approaches from the literature. 2 Circus Background A Circus specification is in some sense an extension of Z [40] in that it takes the paragraphs of Z and adds new paragraph forms that can define Circus channels, processes and actions. Channels correspond to CSP events: channel c : T Circus actions can be c...

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“…The UTP research agenda has as its ultimate goal to cover all the interesting paradigms of computing, including both declarative and procedural, hardware and software. It presents a theoretical foundation for understanding software and systems engineering, and has already been exploited in areas such as hardware [56,80], hardware/software co-design [8] and component-based systems [76]. But it also presents an opportunity when constructing new languages, especially ones with heterogeneous paradigms and techniques.…”

Section: Unifying Theories Of Programming (Utp)mentioning

confidence: 99%

UTP by Example: Designs

Woodcock

Foster

2017

Engineering Trustworthy Software Systems

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Abstract. We present a tutorial introduction to the semantics of a basic nondeterministic imperative programming language in Unifying Theories of Programming (UTP). First, we give a simple relational semantics that accounts for a theory of partial correctness. Second, we give a semantics based on the theory of precondition-postcondition pairs, known in UTP as designs. This paper should be read in conjunction with the UTP book by Hoare & He. Our contribution lies in the large number of examples we introduce.

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Linking a state-rich process algebra to a state-free algebra to verify software/hardware implementation

Cited by 7 publications

References 16 publications

Model checking of state-rich formalism by linking to $$CSP\,\Vert \,B$$ C S P ‖ B

Model checking of state-rich formalism by linking to $$CSP\,\Vert \,B$$ C S P ‖ B

Towards a Model-Checker for Circus

UTP by Example: Designs

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