Service-Oriented Modeling of CoCoME with Focus and AutoFocus

Broy, Manfred; Fox, Jorge; Hölzl, Florian; Koss, Dagmar; Kuhrmann, Marco; Meisinger, Michael; Penzenstadler, Birgit; Rittmann, Sabine; Schätz, Bernhard; Spichkova, Maria; Wild, Doris

doi:10.1007/978-3-540-85289-6_8

Cited by 16 publications

(10 citation statements)

References 10 publications

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“…Several solutions for optimizing cyberphysical systems [6,41,59] rely on simulations but are intimately tied to the underlying domain. For example, AutoFocus [10,41] targets reactive systems, in which modeling elements are domainspecific abstractions (e.g., electronic control unit) and constraints are linked to the domain's semantics (e.g., traffic-light behavior). As another example, OpenMDAO [35,59] numerically optimizes designs of multidisciplinary systems such as aircraft, and relies on the availability of mathematical models for properties such as lift, thermodynamics, etc.…”

Section: Empiricalmentioning

confidence: 99%

“…While they come with shortcomings of their own (e.g., false negatives, long execution times), dynamic analysis techniquesÐi.e., architectural model simulations [27,55]Ðare more capable of capturing the nondeterminism reflective of reality [46]. Despite notable efforts, especially in domains with well understood properties (e.g., stream-based systems [22], reactive hybrid systems [10,41,87], numerical optimization of aircraft designs [35,59]) simulations of software architectural models [57,91] have not been employed as widely as static analyses [2], for at least four reasons. First, creating simulatable system design models is difficult [27].…”

Section: Related Workmentioning

confidence: 99%

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eQual: informing early design decisions

Shahbazian

Karthik

Brun

et al. 2020

Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Softw

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When designing a software system, architects make a series of design decisions that directly impact the system's quality. The number of available design alternatives grows rapidly with system size, creating an enormous space of intertwined design concerns that renders manual exploration impractical. We present eQual, a model-driven technique for simulation-based assessment of architectural designs. While it is not possible to guarantee optimal decisions so early in the design process, eQual improves decision quality. eQual is effective in practice because it (1) limits the amount of information the architects have to provide and (2) adapts optimization algorithms to effectively explore massive spaces of design alternatives. We empirically demonstrate that eQual yields designs whose quality is comparable to a set of systems' known optimal designs. A user study shows that, compared to the state-of-the-art, engineers using eQual produce statistically significantly higherquality designs with a large effect size, are statistically significantly more confident in their designs, and find eQual easier to use. CCS CONCEPTS • Software and its engineering → Software design engineering; Software implementation planning.

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Section: Empiricalmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

eQual: informing early design decisions

Shahbazian

Karthik

Brun

et al. 2020

Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Softw

View full text Add to dashboard Cite

show abstract

“…The states of the buffer (a number of elements) are implemented as a vector (l.7). In the initialization part, the actual parameters (agents and servers) are passed to every server (l. [29][30][31] and the initial state of every server is defined after the dot. For every agent, the initial message is defined (l.32,33).…”

Section: Imds As a Programming Languagementioning

confidence: 99%

“…Many modeling environments use one of the distribution paradigms: message passing in the client-server paradigm or RPC paradigm with messages serving as process migration means. Even in asynchronous formalisms, like SDL (Specification and Description Language [30]), Focus [31], Promela [32] or π-calculus [33], communication duality is not expressed.…”

mentioning

confidence: 99%

Specification and Verification in Integrated Model of Distributed Systems (IMDS)

Daszczuk

2018

Computers

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Distributed systems, such as the Internet of Things (IoT) and cloud computing, are becoming popular. This requires modeling that reflects the natural characteristics of such systems: the locality of independent components, the autonomy of their decisions, and asynchronous communication. Automated verification of deadlocks and distributed termination supports rapid development. Existing techniques do not reflect some features of distribution. Most formalisms are synchronous and/or use some kind of global state, both of which are unrealistic. No model supports the communication duality that allows the integration of a remote procedure call and client-server paradigm into a single, uniform model. The majority of model checkers refer to total deadlocks. Usually, they do not distinguish between communication deadlocks from resource deadlocks and deadlocks from distributed termination. Some verification mechanisms check partial deadlocks at the expense of restricting the structure of the system being verified. The paper presents an original formalism for the modeling and verification of distributed systems. The Integrated Model of Distributed Systems (IMDS) defines a distributed system as two sets: states and messages, and the relationship of the “actions” between these sets. Communication duality provides projections on servers and on traveling agents, but the uniform specification of the verified system is preserved. General temporal formulas over IMDS, independent of the structure of the verified system, allow automated verification. These formulas distinguish between deadlocks and distributed termination, and between communication deadlocks and resource deadlocks. Partial deadlocks and partial termination can be checked. The Dedan tool was developed using IMDS formalism.

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“…There are several applications of formal methods to HCI analysis, e.g., (Shackel & Richardson, 1991;Følstad et al, 2012), as well as many approaches to system modelling, e.g., (Broy et al, 2008;Berger, 2002;Donath et al, 2008). In our previous work (references have been removed for double-blind review), we focused on human factors in modelling processes as well on readability and usability of formal languages and interfaces, with the aim of combining the achievements in the fields of HCI and modelling approaches.…”

Section: Introductionmentioning

confidence: 99%