Goal-oriented requirement language model analysis using analytic hierarchy process

IF 0.6 Q4 COMPUTER SCIENCE, THEORY & METHODS Multiagent and Grid Systems Pub Date : 2023-02-03 DOI:10.3233/mgs-220242
Sreenithya Sumesh, A. Krishna, R.Z. ITU-T
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Abstract

We present the application of multi-objective optimisation analytic methodologies to goal models in this research, with the intention of providing various benefits beyond the initial modelling act. Optimisation analysis can be used by modellers to evaluate goal satisfaction, evaluate high-level design alternatives, aid analysts in deciding on high-level requirements and system design, verify the sanity of a model, and improve communication and learning. Goal model analysis may be done in a variety of methods, depending on the nature of the model and the study’s goal. In our work, we use the Goal-Oriented Requirement Language (GRL), which is part of the User Requirements Notation (URN), a new International Telecommunication Union (ITU) recommendation that offers the first standard goal-oriented language. Existing optimisation methods are geared towards maximising objective functions. On the other hand, real-world problems necessitate simultaneous optimisation of both maximising and minimising objective functions. This work explores a GRL model analysis that may accommodate the conflicting goals of various inter-dependent actors in a goal model using the Analytic Hierarchy Process (AHP). By evaluating the qualitative or quantitative satisfaction levels of the actors and intentional elements (e.g., objectives and tasks) that make up the model, we construct a multi-objective optimisation method for analysis using the GRL model. The proposed hybrid technique evaluates the contribution of alternatives to the accomplishment of top softgoals. It is then integrated with the top softgoals’ normalised relative priority values. The integration result may be utilised to assess multiple alternatives based on the requirements problem. Although the URN standard does not mandate a specific propagation algorithm, it does outline certain criteria for developing evaluation mechanisms. Case studies were used to assess the viability of the suggested approach in a simulated environment using JAVA Eclipse and IBM Cplex. The findings revealed that the proposed method can be used to analyse goals in goal models with opposing multi-objective functions.
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基于层次分析法的面向目标需求语言模型分析
在本研究中,我们提出了多目标优化分析方法在目标模型中的应用,旨在提供超出初始建模行为的各种好处。建模人员可以使用优化分析来评估目标满意度,评估高级设计备选方案,帮助分析人员决定高级需求和系统设计,验证模型的合理性,并改善沟通和学习。根据模型的性质和研究的目标,目标模型分析可以用多种方法进行。在我们的工作中,我们使用面向目标的需求语言(GRL),它是用户需求符号(URN)的一部分,URN是国际电信联盟(ITU)的一项新建议,提供了第一个标准的面向目标的语言。现有的优化方法都是为了使目标函数最大化。另一方面,现实世界的问题需要同时优化最大化和最小化目标函数。这项工作探讨了GRL模型分析,该模型可以使用层次分析法(AHP)在目标模型中容纳各种相互依赖的参与者的冲突目标。通过评估组成模型的参与者和意向元素(例如,目标和任务)的定性或定量满意度水平,我们构建了一个多目标优化方法,用于使用GRL模型进行分析。所提出的混合技术评估了备选方案对实现顶级软件目标的贡献。然后将其与顶级软目标的标准化相对优先级值集成。集成结果可以用来评估基于需求问题的多个备选方案。尽管URN标准没有强制要求特定的传播算法,但它确实概述了开发评估机制的某些标准。案例研究用于在使用JAVA Eclipse和IBM Cplex的模拟环境中评估所建议方法的可行性。研究结果表明,该方法可用于具有对立多目标函数的目标模型中的目标分析。
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来源期刊
Multiagent and Grid Systems
Multiagent and Grid Systems COMPUTER SCIENCE, THEORY & METHODS-
CiteScore
1.50
自引率
0.00%
发文量
13
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