“…The last issue reported in the planning stage focused on factors other than uncertainty and was more connected to various features. Authors in this context discussed that having many variabilities to be considered in the decision-making of many functions, like functional language, can make the design and planning stage challenging [95]. Furthermore, decision selection challenges regarding individuals from various backgrounds and expertise were also an issue [96].…”
A smart contract is a digital program of transaction protocol (rules of contract) based on the consensus architecture of blockchain. Smart contracts with Blockchain are modern technologies that have gained enormous attention in scientific and practical applications. A smart contract is the central aspect of a blockchain that facilitates blockchain as a platform outside the cryptocurrency spectrum. The development of blockchain technology, with a focus on smart contracts, has advanced significantly in recent years. However, research on the smart contract idea has weaknesses in the implementation sectors based on a decentralized network that shares an identical state. This paper extensively reviews smart contracts based on multi-criteria analysis, challenges and motivations. Therefore, implementing blockchain in multicriteria research is required to increase the efficiency of interaction between users via supporting information exchange with high trust. Implementing blockchain in the multi-criteria analysis is necessary to increase the efficiency of interaction between users via supporting information exchange and with high confidence, detecting malfunctioning, helping users with performance issues, reaching a consensus, deploying distributed solutions and allocating plans, tasks and joint missions. The smart contract with decision-making performance, planning and execution improves the implementation based on efficiency, sustainability and management. Furthermore, the uncertainty and supply chain performance lead to improved users' confidence in offering new solutions in exchange for problems in smart contacts. Evaluation includes code analysis and performance, while development performance can be under development.
“…The last issue reported in the planning stage focused on factors other than uncertainty and was more connected to various features. Authors in this context discussed that having many variabilities to be considered in the decision-making of many functions, like functional language, can make the design and planning stage challenging [95]. Furthermore, decision selection challenges regarding individuals from various backgrounds and expertise were also an issue [96].…”
A smart contract is a digital program of transaction protocol (rules of contract) based on the consensus architecture of blockchain. Smart contracts with Blockchain are modern technologies that have gained enormous attention in scientific and practical applications. A smart contract is the central aspect of a blockchain that facilitates blockchain as a platform outside the cryptocurrency spectrum. The development of blockchain technology, with a focus on smart contracts, has advanced significantly in recent years. However, research on the smart contract idea has weaknesses in the implementation sectors based on a decentralized network that shares an identical state. This paper extensively reviews smart contracts based on multi-criteria analysis, challenges and motivations. Therefore, implementing blockchain in multicriteria research is required to increase the efficiency of interaction between users via supporting information exchange with high trust. Implementing blockchain in the multi-criteria analysis is necessary to increase the efficiency of interaction between users via supporting information exchange and with high confidence, detecting malfunctioning, helping users with performance issues, reaching a consensus, deploying distributed solutions and allocating plans, tasks and joint missions. The smart contract with decision-making performance, planning and execution improves the implementation based on efficiency, sustainability and management. Furthermore, the uncertainty and supply chain performance lead to improved users' confidence in offering new solutions in exchange for problems in smart contacts. Evaluation includes code analysis and performance, while development performance can be under development.
“…Voelter et al use SMT solving with the Z3 solver for advanced error checking and verification in the KernelF language [16], a reusable functional language for the development of DSLs. Voelter et al apply SMT solving successfully in a DSL on a case study for the domain of payroll calculations [17], i.e.…”
Within the printing industry, much of the variety in printed applications comes from the variety in finishing. Finishing comprises the processing of sheets of paper after being printed, e.g. to form books. The configuration space of finishers, i.e. all possible configurations given the available features and hardware capabilities, are large. Current control software minimally assists operators in finding useful configurations. Using a classical modelling and integration approach to support a variety of configuration spaces is suboptimal with respect to operatability, development time, and maintenance burden.In this paper, we explore the use of a modeling language for finishers to realize optimizing decision making over configuration parameters in a systematic way and to reduce development time by generating control software from models.We present CSX, a domain-specific language for high-level declarative specification of finishers that supports specification of the configuration parameters and the automated exploration of the configuration space of finishers. The language serves as an interface to constraint solving, i.e., we use low-level SMT constraint solving to find configurations for high-level specifications. We present a denotational semantics that expresses a translation of CSX specifications to SMT constraints. We describe the implementation of the CSX compiler and the CSX programming environment (IDE), which supports well-formedness checking, inhabitance checking, and interactive configuration space exploration. We evaluate CSX by modelling two realistic finishers. Benchmarks show that CSX has practical performance (<1s) for several scenarios of configuration space exploration.
“…An example of this is a kernelbased strategy for language-oriented programming that aims at developing programs specific to niche hardware platforms based on a kernel and a kernel-driven IDE [15]. KernelF [33] provides a language core plus extensions for expression languages to develop new languages with embedded expressions. Language Families.…”
Section: Towards Family-based Approaches To Variability Modellingmentioning
confidence: 99%
“…Language Families. A kernel and a library of extensions can be developed as a language family, e.g., a family of expression languages [33] or a family of state-machine modelling languages [11,35]. Engineering variable languages as language families shifts emphasis from developing and analysing a single language to developing and to analysing composable development artefacts for a language family.…”
Section: Towards Family-based Approaches To Variability Modellingmentioning
confidence: 99%
“…Besides general-purpose variability modelling, modelling variability is frequently required by modelling languages targeting specific, but different application domains. While such domain-specific modelling languages operate on domain-specific data structures (e.g., to capture attributes or to define constraints), there is potential for reuse of basic variability abstractions, similar to the idea of a "calculation core" for domain-specific expressions [33].…”
v1e is a language kernel for textual variability modelling built on top of the language-development system DjDSL. As a language kernel, v1e provides a minimal but extensible set of abstractions to implement families of domain-specific languages for textual variability modelling. v1e provides for a small and versatile abstract syntax to encode feature models using multiplicity constraints and canonical semantics. v1e offers built-in analysis support, such as configuration validation, by maintaining internal BDD representations. A derived language becomes realised as a collection of extensions dependent on the language kernel. v1e is designed to be highly extensible and embeddable, e.g., as a dynamic library or as a REPL shell. In this paper, we showcase a selected derived language and the design decisions involved: a kernel implementation of TVL on top of v1e. We conclude the paper by pointing out current limitations (e.g., representing attributed variability models) and future directions (e.g., analysis support beyond BDD).
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