A picture is worth a thousand words. Non-Functional Requirements (NFRs), such as security and usability, are modeled using Softgoal Interdependency Graphs (SIGs) to capture potential conflicts and synergies. However, the practice of NFR modeling remains limited, partly due to unfamiliarity with modeling languages like SIG and insufficient understanding of relevant NFRs. Large Language Models (LLMs) show some knowledge of NFRs and SIG concepts, such as goal decomposition and operationalization, but often lack precise knowledge of formal SIG syntax. We introduce SIG-GPT, a GPT-4-based LLM augmented with SIG knowledge using text-based grammar supplied and Retrieval Augmented Generation (RAG). RAG enhancs LLM responses by retrieving relevant external knowledge, while the grammar enforces correct syntax, guiding the LLM to generate SIGs align with formal notation. To help practitioners better understand SIG modeling, reduce time and effort, and enhance NFR proficiency, we apply textual grammar to SIG-GPT, ensuring it is ready for seamless integration with visual modeling tools like RE- Tool, enabling the LLM to generate correct SIG structures without requiring a large dataset of SIG examples. Results show that SIG-GPT with grammar and RAG achieves 100% syntactic accuracy, 95% semantic accuracy, and 98% cohesion (CCR) while aligning with Bloom's Taxonomy to enhance structured reasoning in SIG modeling.

Nonfunctional requirements (NFRs), such as security and usability, address how well a software system does its intended functions.Automatic NFR classification using machine learning (ML) has been proposed to address the potential problem of faulty manual classification of NFRs.In order for ML to be effective, however, we need large, relevant datasets for training.Training data shortage has been identified as a limiting factor for these approaches.Even when available, procuring proprietary NFR data is challenging and time-consuming.In this paper, we propose a process to generate synthetic labeled NFR datasets using GPT-4o, a large language model (LLM), to alleviate the data shortage issue.This process consists of a set of prompt templates, iterative contextualization of definition, and NFR generation by class.We produced 2 new labeled NFR datasets, one from synthetic generation and the other from real documents for validation.To determine both the strengths and weaknesses of the synthetic dataset, we used it to train ML models using 3 different algorithms for the multi-class NFR classification task.Although limited, we feel our experimental results show that synthetic data as well as augmented data perform better than just real data alone.

Patterns aid in the development of software for micro-businesses. However, capturing recurring concepts in good representations for the purpose of requirements engineering is challenging. Such representations need to be intuitive enough so that non-technical people such as micro-business owners can understand them. At the same time, the representations have to be technical enough so that software developers can use them for software design and development. In this paper, we propose a requirements-based approach for representing micro-business patterns. We describe pattern emergence from observation and analysis. We list down and specify both the functional and nonfunctional requirements of the micro-business in a table and then represent the workflows using diagrams. We use a combination of a business process modeling notation, softgoal interdependency graphs, and our own customized symbols. Four real-world micro-business cases are discussed in the paper in order to see both the strengths and weaknesses of our approach.

Requirements engineers need to understand and model different aspects of organizations and systems under construction, and may need to use different modeling notations. However, most modeling tools support only one (or at most a few notations), hindering requirements engineers from using the most appropriate notations for the particular modeling task. The RE-Tools is an open-source toolkit implemented using a UML Profile for StarUML, an open-source UML modeling tool. The toolkit supports many leading requirements modeling notations, including the NFR Framework, the i* Framework, KAOS, Problem Frames, and UML. Each of these notations may be used for modeling independent corresponding diagrams or together with non-functional requirements (NFRs). The toolkit also supports the original qualitative reasoning of the NFR Framework and augments with a quantitative one.

Non-functional requirements (NFRs), such as security and cost, are generally subjective and oftentimes synergistic or conflicting with each other. Properly dealing with such NFRs requires a large body of knowledge – goals to be achieved, problems or obstacles to be avoided, alternative solutions to mitigate the problems, and the best compromising alternative solution to be selected. However, few patterns exist for dealing with these kinds of knowledge of NFRs. In this paper, we present four kinds of NFR patterns for capturing and reusing knowledge of NFRs – objective pattern, problem pattern, alternatives pattern and selection pattern. NFR patterns may be visually represented, and organized by rules of specialization to create more specific patterns, of composition to build larger patterns, and of instantiation to create new patterns using existing patterns as templates. We have applied the NFR pattern approach to the TJX incident, one of the largest credit card theft in history, as a realistic case study.

Traceability between hazards and countermeasures is important for hazard analysis to give confidence and help justify whether relevant hazards have been considered and sufficiently mitigated. However, use case modeling, the current de facto standard technique for requirements elicitation lacks negative modeling constructs for representing hazards. This paper presents a case study to apply a goal-oriented method for car security related hazard analysis where hazards are represented as operationalizations with negative contribution toward system non-functional requirements (NFRs). In turn, countermeasures are represented as operationalizations with negative contribution toward the respective hazards to negate their negative effect on the NFRs. The study finds that using the goal-oriented approach is most suitable for risk-driven applications, but also compatible and complementary to other applications.

In order for a software system to be of value, it should meet both functional requirements (FRs) and non-functional requirements (NFRs). Concerning FRs, UML has been used as the de facto object-oriented analysis and design notation. Concerning NFRs, the NFR framework extends, and complements, UML, by treating NFRs as potentially conflicting or synergistic softgoals to be achieved, thereby allowing for the consideration of alternatives and analysis of trade-offs among the alternatives. Albeit the complementary nature of the two, UML and the NFR framework offer two different notations -syntactically, semantically and visually, which makes it difficult to produce requirements models that integrate both FRs and NFRs. In this paper, we propose an integrated modeling language by extending UML with the NFR framework using the standard extension mechanism called UML profile where we define a metamodel to represent the concepts in the NFR framework and identify the extension points for integrating the two notations. We also show how CASE tools may use this profile in building an integrated requirements model based on a model of the well known London ambulance case study.