Goal Model Analysis Research Articles

VeGAn-Tool: A fuzzy-logic approach for value-based goal model analysis

VeGAn-Tool: A fuzzy-logic approach for value-based goal model analysis

Goal-oriented requirement language model analysis using analytic hierarchy process

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.

Challenges and review of goal-oriented requirements engineering based competitive non-functional requirements analysis

Modelling and analysis in software system development can be especially challenging in early requirements engineering (RE), where high-level system non-functional requirements are discovered. In the early stage, hard to measure non-functional requirements are critical; understanding the interactions between systems and stakeholders is key to system success. Goal-oriented requirements engineering (GORE) has been successful in dealing with the issues that may arise during the analysis of requirements. While assisting in the analysis of requirements, i* goal model is the only framework available among the many GORE models, emphasising socio-technical domains such as stakeholders/actors/players, goals/objectives, dependencies and design options/alternatives. Most current approaches to goal-model analysis use quantitative methods or formal information that is hard to gather in early RE, or produce analysis results automatically over models. In real-time competitive applications, the goals of various stakeholders are conflicting in complex systems. Also, each of the system goals have various alternative design options for the systems and optimal selection of goal-oriented requirements faces several challenges in requirements-based engineering. Hence, effective decision-making frameworks are necessary to capture the real issues to achieve multi-objective optimisation of interdependent actors. To obtain an optimum strategy for interdependent actors in the i* goal model must balance the opposing goals reciprocally. To achieve this, the model needs to go beyond the analytical decision-making tools such as sensitivity analysis tasks, cost-effective analysis process, game-theoretic concepts and analytical hierarchical process. To address these requirements, this paper discusses the design of novel frameworks for an agent-based goal model analysis in requirements engineering. The objective of this paper is to provide a brief and comprehensive review of the major efforts undertaken along this line of research. In this paper we have prepared literature review of the concepts, terminology, significance and techniques of Goal oriented requirements engineering in the context of non-functional requirements analysis.

Requirements decision-making as a process of Argumentation: A Google Maps Case Study with Goal Model

In social media platforms, crowd-users extensively interact and contribute information related to software applications. Usually, crowd-users discuss software features or hot issues and record their opinions about the software applications under discussion either in textual form or via end-user votes. Such requirements-related information is considered a pivotal alternative source for requirements engineers to the already existing in-house stakeholders in order to illustrate decision-making. Also, requirements decision-making for Crowd requirements engineering is a difficult task, as it is always based on incomplete knowledge and requires trade-offs from multi-perspectives. However, existing requirements models and associated tools are still lacking, which enable requirements engineers to make informed decision-making and capture conflicting requirements knowledge. This paper elaborates the interaction among the crowd-users about the Google Map mobile application in the Reddit forum to recover conflicting requirements-related information using the goal modeling approach. For this purpose, we extracted critical arguments from a crowd-users conversation in user forums regarding a given design; built a graphical argumentation model based on the extracted information; aligned types of arguments with goal-oriented modeling constructs in the non-functional requirements framework; conducted exiting goal-model analysis to the requirements model to reach consensus based on argumentation and reasoning, such as supporting, attacking, undefined, and conflicting. The proposal is described with illustrative example models and the associated evaluation processes of design decision-making situation for Google Map interface design.

Goal Model Evaluation Based on State-Space Representation

Goal models have been used for the last two decades in various disciplines to represent business, organizations, and individuals' objectives. Several methodologies and standards have emerged, and various goal analysis and evaluation algorithms have been introduced serving different sectors, including decision support. In contrast to most researches which are based mainly on simulation to predict the satisfaction levels of final goals, this research proposes a new framework for evaluating goal models based on the state-space representation that is used extensively in control systems. This new approach brings the theories and literature of state-space representation of systems to goal models, opening a new direction for using its available mature techniques and tools, for goal model analysis and evaluation. A hypothetical goal model, which can be used for policymaking after the COVID-19 pandemic, is presented as an example of how the proposed framework can be used, and the results that can be obtained.

Goal model analysis of autonomy requirements for Unmanned Aircraft Systems

Designing Unmanned Aircraft Systems (UASs) for optimal autonomy while meeting user requirements is quite challenging. Researchers have focused on improving autonomy algorithms and verification methods to ensure safe and reliable autonomous behavior in UASs, but little research has been conducted on requirements engineering for UASs to answer design questions and explore the trade space for using autonomy to satisfy user requirements. This paper introduces a method to determine an optimal set of autonomous capabilities that satisfies UAS user requirements in the early stages of conceptual design. The method uses a modified Autonomy Requirements Engineering (ARE) process that applies quantitative measures and statistical analysis to Goal-Oriented Requirements Engineering (GORE). We demonstrate this method in a case study of a “disaster robot,” i.e., a hazard response UAS for which the autonomy requirements were optimized using a goal model developed in the Goal-oriented Requirement Language (GRL), as implemented in the modeling tool jUCMNav. The high-level goals of the hazard response UAS—system performance, cost, and safety—were evaluated using the formula-based GRL strategy evaluation algorithm resident in jUCMNav version 6.0. An autonomy trade space study was conducted through a Design and Analysis of Simulation Experiments (DASE). Our designed simulation experiment inserted the number of trials (evaluation strategies) and inputs into the goal model, and evaluation data were analyzed to optimize design factors based on user weightings of the response variables. This paper presents a structured method of ARE for UASs, which could be adopted more broadly across other domains, demonstrating how to optimize autonomous capabilities for different design conditions.

Interactive goal model analysis for early requirements engineering

In goal-oriented requirements engineering, goal models have been advocated to express stakeholder objectives and to capture and choose among system requirement candidates. A number of highly automated procedures have been proposed to analyze goal achievement and select alternative requirements using goal models. However, during the early stages of requirements exploration, these procedures are difficult to apply, as stakeholder goals are typically high-level, abstract, and hard-to-measure. Automated procedures often require formal representations and/or information not easily acquired in early stages (e.g., costs, temporal constraints). Consequently, early requirements engineering (RE) presents specific challenges for goal model analysis, including the need to encourage and support stakeholder involvement (through interactivity) and model improvement (through iterations). This work provides a consolidated and updated description of a framework for iterative, interactive, agent-goal model analysis for early RE. We use experiences in case studies and literature surveys to guide the design of agent-goal model analysis specific to early RE. We introduce analysis procedures for the i* goal-oriented framework, allowing users to ask "what if?" and "are certain goals achievable? how? or why not?" The i* language and our analysis procedures are formally defined. We describe framework implementation, including model visualization techniques and scalability tests. Industrial, group, and individual case studies are applied to test framework effectiveness. Contributions, including limitations and future work, are described.

Evaluating goal models within the goal-oriented requirement language

In this article, we introduce the application of rigorous analysis procedures to goal models to provide several benefits beyond the initial act of modeling. Such analysis can allow modelers to assess the satisfaction of goals, facilitate evaluation of high-level design alternatives, help analysts decide on the high-level requirements and design of the system, test the sanity of a model, and support communication and learning. The analysis of goal models can be done in very different ways depending on the nature of the model and the purpose of the analysis. In our work, we use the Goal-oriented Requirement Language (GRL), which is part of the User Requirements Notation (URN). URN, a new Recommendation of the International Telecommunications Union, provides the first standard goal-oriented language. Using GRL, we develop an approach to analysis that can be done by evaluating qualitative or quantitative satisfaction levels of the actors and intentional elements (e.g., goals and tasks) composing the model. Initial satisfaction levels for some of the intentional elements are provided in a strategy and then propagated to the other intentional elements of the model through the various links that connect them. The results allow for an assessment of the relative effectiveness of design alternatives at the requirements level. Although no specific propagation algorithm is imposed in the URN standard, different criteria for defining evaluation mechanisms are described. We provide three algorithms (quantitative, qualitative, and hybrid) as examples, which satisfy the constraints imposed by the standard. These algorithms have been implemented in the open-source jUCMNav tool, an Eclipse-based editor for URN models. The algorithms are presented and compared with the help of a telecommunication system example. © 2010 Wiley Periodicals, Inc.