The Timed Concurrent Constraint Language tccp is a declarative synchronous concurrent language, particularly suitable for modelling reactive systems. In tccp, agents communicate and synchronise through a global constraint store. It supports a notion of discrete time that allows all non-blocked agents to proceed with their execution simultaneously.
In this paper, we present a modular architecture for the simulation of tccp programs. The tool comprises three main components. First, a set of basic abstract instructions able to model the tccp agent behaviour, the memory model needed to manage the active agents and the state of the store during the execution. Second, the agent interpreter that executes the instructions of the current agent iteratively and calculates the new agents to be executed at the next time instant. Finally, the constraint solver components which are the modules that deal with constraints.
In this paper, we describe the implementation of these components and present an example of a real system modelled in tccp.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Gallardo, María del Mar

Lavado, Leticia

Panizo, Laura

Electronic Proceedings in Theoretical Computer Science

English

arXiv

Gallardo-Melgarejo, María del Mar

Panizo-Jaime, Laura

Repositorio Institucional Universidad de Málaga

Submitted to:PROLE 2017© M. M. Gallardo, L. Lavado, & L. PanizoThis work is licensed under theCreative Commons Attribution License.A Simulation Tool for tccp Programs∗Marı´a-del-Mar Gallardo Leticia LavadoLaura PanizoUniversidad de Ma´laga, Andalucı´a Tech, Dept. Lenguajes y Ciencias de la Computacio´n, Espan˜a.[gallardo,leticialavmu,laurapanizo]@lcc.uma.esThe Timed Concurrent Constraint Language tccp is a declarative synchronous concurrent language,particularly suitable for modelling reactive systems. In tccp, agents communicate and synchronisethrough a global constraint store. It supports a notion of discrete time that allows all non-blockedagents to proceed with their execution simultaneously.In this paper, we present a modular architecture for the simulation of tccp programs. The toolcomprises three main components. First, a set of basic abstract instructions able to model the tccpagent behaviour, the memory model needed to manage the active agents and the state of the storeduring the execution. Second, the agent interpreter that executes the instructions of the current agentiteratively and calculates the new agents to be executed at the next time instant. Finally, the constraintsolver components which are the modules that deal with constraints. In this paper, we describe theimplementation of these components and present an example of a real system modelled in tccp.The full version of this paper [4] has been published in the proceedings of the 24th InternationalWorkshop on Functional and (Constraint) Logic Programming (WFLP 2016).Key Words: Timed Concurrent Constraint Language (tccp), Simulation tool, Ab-stract tccp instructions1 IntroductionIt is well known that many critical applications in different domains, such as health [13], railways [10]or automotive [8] have a reactive and concurrent behaviour that is difficult to model and analyse. Un-fortunately, certain errors in these applications may have highly negative consequences and, therefore,it is essential to detect failures in software in the early design phases. This is why most modelling lan-guages for these complex systems are supported by simulation and verification tools that guarantee thesoftware’s safety and reliability with respect to the critical properties.Several formalisms have been developed to solve the problem of describing and analysing concur-rent systems. In this paper, we focus on the Concurrent Constraint Paradigm (ccp) [11] characterisedby the use of store-as-constraint instead of the classical store-as-value paradigm. Specifically, tccp [3]is a language suitable for describing reactive systems within this paradigm. As opposed to the inter-leaving composition of processes supported by most concurrent modelling languages, tccp makes use ofthe synchronous composition of processes. Synchronous languages have proved to be very useful formodelling hardware and software systems. Some successful examples are Lustre [7] or SIGNAL [5]. Thesynchronous management of processes clearly simplifies the scheduling tasks, although it complicatesthe memory use. The declarative and synchronous character of tccp makes it particularly suitable notonly for describing but also for analysing complex concurrent systems.There are a few tools for tccp proposed in the literature [9, 12]. In this paper, we present a modularframework for tccp with the aim of overcoming the lack of simulation and analysis tools. Classically, the∗This work has been supported by the Andalusian Excellence Project P11-TIC7659.2 A Simulation Tool for tccpimplementation of logic languages has been based on the definition of the so-called abstract machineswhich provide an abstraction layer on the ultimate device that will execute the programs. Warren AbstractMachine (WAM) [2] is the first and most well-known abstract machine for logic languages. In the contextof concurrent logic languages, there exist other proposals such as the abstract machine based on theconstruction of an AND/OR tree for the implementation of Parlog [6], or the Parallel Inference Machine(PMI) for language KL1 [14].We have built a simulation tool for tccp programs following the abstract machine philosophy butwith some differences derived from the special features of the language1. The construction of a toolfor executing tccp implies dealing with its declarative, constraint-based and synchronous character. Forinstance, the logic and concurrent nature of tccp involves the creation of a large number of fine-grainagents with a well-delimited variable scope. In addition, the use of constraints as data makes the integra-tion of constraint solvers in the tool necessary. Furthermore, to correctly deal with the synchronisation,all agents executing in parallel must have a consistent view of the global memory (called store in tccp).The implemented tool comprises different components to successfully solve the aforementionedproblems. The core of the tool is formed by a set of abstract instructions and a memory model thatis able to represent the state of the tccp program (that is, the current agent and the state of the globalstore) during the execution. In addition, the tool includes an interpreter that executes the current activeagent iteratively. Finally, there is a module with the constraint solvers used to manage the basic opera-tions on the global store correctly. In this paper, we describe all these components, their implementationand evaluation with a typical tccp example.2 Conclusions and Future WorkWe have presented an abstract machine for tccp, which defines the behaviour of tccp agents over amemory architecture called store. The abstract machine is composed of different modules which havebeen design to be as independent as possible. Most of the architecture components are unaware of theactual implementation of the memory or the particular implementation of the agent behaviour. We thinkthat this approach facilitates and simplifies the development of tools for tccp. In addition, we haveimplemented a tool for the simulation of tccp following this abstract machine architecture. The toolhas been implemented in Java, and uses other external libraries and frameworks to implement differentelements. For example, we use ANTLR to generate the parsers, and PPL to implement the constraintsolver for linear constraints.We have evaluated the simulator with the photocopier example, running different number of abstractmachine steps. We have presented the state of the abstract machine store after executing the example,and shown some performance measures obtained with profiling tools. We believe that the performanceis acceptable, although we should improve the memory model to achieve more efficient implementationsfor constructing, for instance, a tccp model checker.Although, the current tool at http://morse.uma.es/tools/tccp may be only used to simulatesome available tccp codes, we plan to extend its capability by allowing users to simulate their ownprograms. In fact, the tool only lacks a frontend that manages syntax errors. In addition, due to thedifferent implementation approaches followed by tool [9] and ours, it is difficult to compare performanceof both tools but we plan to do it in the near future.As future work, we wish to extend the abstract machine to Hy-tccp [1]. Hy-tccp is an extension oftccp for hybrid systems, which adds a notion of continuous time and new agents to describe the contin-1The prototype tool can be found at http://morse.uma.es/tools/tccp.M. M. Gallardo, L. Lavado, & L. Panizo 3uous dynamics of hybrid systems. Hy-tccp is independent from the kind of constraints over continuousvariables. To implement a Hy-tccp simulator, we will assume that the hybrid systems are rectangular.Because of the independence amongst the different entities which compose implementation, the exten-sion of the current abstract machine will involve adding the new agents of the Hy-tccp language andprobably new abstract machine instructions. In addition, the parser should be extended to recognise thenew agents. Finally, we will reuse PPL as the constraint solver for constraints over continuous variables.References[1] D. Adalid, M. Gallardo & L. Titolo (2015): Modeling Hybrid Systems in the Concurrent ConstraintParadigm. Electronic Proceedings in Theoretical Computer Science 173, doi:10.4204/EPTCS.173.1.[2] H. Aı¨t-Kaci (1991): Warren’s Abstract Machine: A Tutorial Reconstruction. MIT Press, Cambridge, MA,USA.[3] F. de Boer, M. Gabbrielli & M. Meo (2000): A Timed Concurrent Constraint Language. Information andComputation 161(1), pp. 45 – 83, doi:10.1006/inco.1999.2879.[4] M. Gallardo, L. Lavado & L. Panizo (2017): A Simulation Tool for tccp Programs. In S. Schwarz &J. Voigtla¨nder, editors: Proceedings 29th and 30th Workshops on (Constraint) Logic Programming and 24thInternational Workshop on Functional and (Constraint) Logic Programming, and 24th International Work-shop on Functional and (Constraint) Logic Programming, WLP 2015 / WLP 2016 / WFLP 2016, Dresdenand Leipzig, Germany, 22nd September 2015 and 12-14th September 2016., EPTCS 234, pp. 120–134,doi:10.4204/EPTCS.234.9. Available at http://dx.doi.org/10.4204/EPTCS.234.9.[5] T. Gautier & P. Le Guernic (1987): SIGNAL: A declarative language for synchronous programming of real-time systems. In G. Kahn, editor: FPCA, Lecture Notes in Computer Science 274, Springer, pp. 257–277,doi:10.1007/3-540-18317-5 15.[6] S. Gregory, I.T. Foster, A.D. Burt & G.A. Ringwood (1989): An abstract machine for the implementation ofPARLOG on uniprocessors. New Generation Computing 6(4), pp. 389–420, doi:10.1007/BF03037448.[7] N. Halbwachs, P. Caspi, P. Raymond & D. Pilaud (1991): The synchronous data flow programming languageLUSTRE. Proceedings of the IEEE 79(9), pp. 1305–1320, doi:10.1109/5.97300.[8] E.Y. Kang, G. Perrouin & P.Y. Schobbens (2013): Model-Based Verification of Energy-Aware Real-TimeAutomotive Systems. In: Engineering of Complex Computer Systems (ICECCS), 2013 18th InternationalConference on, pp. 135–144, doi:10.1109/ICECCS.2013.27.[9] A. Lescaylle & A. Villanueva (2009): The tccp Interpreter. Electronic Notes in Theoretical Computer Science258(1), pp. 63 – 77, doi:10.1016/j.entcs.2009.12.005.[10] J. Qian, J. Liu, X. Chen & J. Sun (2015): Modeling and Verification of Zone Controller: The SCADE Experi-ence in China’s Railway Systems. In: Complex Faults and Failures in Large Software Systems (COUFLESS),2015 IEEE/ACM 1st International Workshop on, pp. 48–54, doi:10.1109/COUFLESS.2015.15.[11] V.A. Saraswat & M. Rinard (1990): Concurrent Constraint Programming. In: Proceedings of the 17th ACMSIGPLAN-SIGACT Symposium on Principles of Programming Languages, POPL ’90, ACM, New York,NY, USA, pp. 232–245, doi:10.1145/96709.96733.[12] T. Sjo¨land, E. Klintskog & S. Haridi (2001): An interpreter for Timed Concurrent Constraints in Mozart(Extended Abstract).[13] L.A. Tuan, M.C. Zheng & Q.T. Tho (2010): Modeling and Verification of Safety Critical Systems: A CaseStudy on Pacemaker. In: Secure Software Integration and Reliability Improvement (SSIRI), 2010 FourthInternational Conference on, pp. 23–32, doi:10.1109/SSIRI.2010.28.[14] K. Ueda & T. Chikayama (1990): Design of the Kernel Language for the Parallel Inference Machine. TheComputer Journal 33(6), pp. 494–500, doi:10.1093/comjnl/33.6.494.

A Simulation Tool for tccp Programs

The Timed Concurrent Constraint Language tccp is a declarative synchronous concurrent language, particularly suitable for modelling reactive systems. In tccp, agents communicate and synchronise through a global constraint store. It supports a notion of discrete time that allows all non-blocked agents to proceed with their execution simultaneously.

In this paper, we present a modular architecture for the simulation of tccp programs. The tool comprises three main components. First, a set of basic abstract instructions able to model the tccp agent behaviour, the memory model needed to manage the active agents and the state of the store during the execution. Second, the agent interpreter that executes the instructions of the current agent iteratively and calculates the new agents to be executed at the next time instant. Finally, the constraint solver components which are the modules that deal with constraints.

In this paper, we describe the implementation of these components and present an example of a real system modelled in tccp

A Simulation Tool for tccp Programs

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