Model-driven engineering of industrial process control applications

Lukman, Tomaž; Godena, Giovanni; Gray, Jeff; Strmĉnik, Stanko

doi:10.1109/etfa.2010.5641224

Cited by 10 publications

(4 citation statements)

References 21 publications

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“…In the area of high-performance computing, Jacob et al designed and implemented a modeling framework called PPmodel to assist programmers in separating the core computation from the details of a specific parallel architecture, identifying and retargeting the parallel section of a program to execute in a different platform [11]. Another example is the application of modeldriven engineering and a supporting tool infrastructure for the industrial process control domain, done by Lukman et al [12]. The work described in this paper distinguishes itself from the following aspects: 1) it focuses on the robotics domain; 2) non-functional requirements (i.e., timing and scheduling requirements) have been integrated with domain concepts and reflected in the generated code; 3) performance analysis and optimization can be made to models during editing time; 4) the same metamodel is mapped to both the textual and the graphical DSL so that the two formats can be interchanged with each other; 5) an iterative development approach and reverse engineering are both supported in our framework.…”

Section: Related Workmentioning

confidence: 99%

A Model-Driven Approach to Support Engineering Changes in Industrial Robotics Software

Sun

Gray

Bulheller³

et al. 2012

Model Driven Engineering Languages and Systems

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Software development has improved greatly over the past decades with the introduction of new programming languages and tools. However, software development in the context of industrial robotics is dominated by practices that require attention to low-level accidental complexities related to the solution space of a particular domain. Most vendor-specific robotics platforms force the developer to be concerned with many low-level implementation details, which presents a maintenance challenge in the context of making engineering changes to the robotics solution. Additionally, satisfying the timing requirements across the platforms of multiple robot vendors represents an additional challenge. We introduce our work using Domain-Specific Modeling to support the control of industrial robots using models that are at a higher level of abstraction than traditional robot programming languages. Our modeling approach assists robotics developers to plan the schedule, validate timing requirements, optimize robot control, handle engineering changes, and support multiple platforms.

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Section: Related Workmentioning

confidence: 99%

A Model-Driven Approach to Support Engineering Changes in Industrial Robotics Software

Sun

Gray

Bulheller³

et al. 2012

Model Driven Engineering Languages and Systems

Self Cite

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“…Most of these works consider the requirement specification to define the system behavior, but without providing methodologies for requirement elicitation and operation mode definition. Most of them only consider the automatic operation mode, except ProcGraph [16] that takes into account the operation modes from the S88 standard.…”

Section: Introductionmentioning

confidence: 99%

A Methodological Approach to Model-Driven Design and Development of Automation Systems

Álvarez

Sarachaga

Burgos

et al. 2018

IEEE Trans. Automat. Sci. Eng.

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The growing complexity of industrial automation demands the adoption of software engineering principles for improving the development process of control systems. This paper presents a methodological approach to the design and development of complex automation systems relying on modeldriven engineering (MDE). A benefit of this approach is the integration of methods and techniques widespread within the automation discipline with modern MDE techniques guiding the designer through the development phases. A second advantage is to add flexibility enough to adapt the steps to the needs of the system under design. Finally, the architecture presented is prepared to be adapted to methodology extensions to cover other aspects of automation systems. The framework is based on domain models that are defined through the development phases using the terminology of the automation field. Using model transformations both documentation about system analysis and design and the skeleton of software units are automatically generated. A proof-of-concept tool has been developed that has been tested on the design of medium-complexity projects to assess the impact of its use with respect to project documentation and maintenance.Note to Practitioners-Control software development can be considered one of the challenges in automation field for achieving leadership in the future economic market. This work presents a model-driven engineering-based approach making use of both automation and software engineering methods and techniques for developing automation control systems. The frame-

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“…Model-driven engineering (MDE) helps address the problems of designing, implementing, and integrating applications (Hästbacka, 2011) (Lukman, 2010) (Schmidt, 2006) (Hailpern, 2006) (Atkinson, 2003) (Kent, 2002). MDE is increasingly used in domains involving modeling software components, developing embedded software systems, and configuring quality-of-service (QoS) policies.…”

Section: Introductionmentioning

confidence: 99%

Quantitative Productivity Analysis of a Domain-Specific Modeling Language

Hoffert

Schmidt

Gokhale

2015

Handbook of Research on Innovations in Systems and Software Engineering

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Model-driven engineering (MDE), in general, and Domain-Specific Modeling Languages (DSMLs), in particular, are increasingly used to manage the complexity of developing applications in various domains. Although many DSML benefits are qualitative (e.g., ease of use, familiarity of domain concepts), there is a need to quantitatively demonstrate the benefits of DSMLs (e.g., quantify when DSMLs provide savings in development time) to simplify comparison and evaluation. This chapter describes how we conducted quantitative productivity analysis for a DSML (i.e., the Distributed Quality-of-Service (QoS) Modeling Language (DQML)). Our analysis shows (1) the significant quantitative productivity gain achieved when using a DSML to develop configuration models compared with not using a DSML, (2) the significant quantitative productivity gain achieved when using a DSML interpreter to automatically generate implementation artifacts as compared to alternative methods when configuring application entities, and (3) the viability of quantitative productivity metrics for DSMLs.

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Model-driven engineering of industrial process control applications

Cited by 10 publications

References 21 publications

A Model-Driven Approach to Support Engineering Changes in Industrial Robotics Software

A Model-Driven Approach to Support Engineering Changes in Industrial Robotics Software

A Methodological Approach to Model-Driven Design and Development of Automation Systems

Quantitative Productivity Analysis of a Domain-Specific Modeling Language

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