ABSTRACT Purpose In the fields of requirements engineering and problem‐oriented requirements analysis, successful conflict identification and resolution are essential to the development and deployment of software systems. The purpose of this paper is to identify conflicting requirements using the Enhanced Pairwise Comparison Method (E‐PCM) applied to a real‐world mobile phone case study within the Jackson problem frame framework. Method This paper enhances the traditional PCM by incorporating a full Natural Language Processing (NLP) pipeline comprising entity extraction, parsing rules, and conflict identification using the spaCy library. The methodology further introduces a modified contribution table to represent identified conflicts, and employs empirical evaluation with a structured conflict scaling system (0–5) in place of fuzzy logic for attribute weighting. Results The results indicate potential conflicts between requirements, categorized as no, minimal, moderate, substantial, high, and extreme conflict, each assigned a corresponding level from 0 to 5. The E‐PCM model achieves a precision of 1.0 (100%) across all nine requirements, with recall ranging from 0.5 to 1.0 and an F1‐Score ranging from 0.66 to 1.0. The overall accuracy of E‐PCM is 87%, outperforming the cluster‐based approach (85.71%). Inter‐rater agreement among 78 undergraduate participants was substantial ( k = 0.769), and Fisher's Exact Test validated the NLP‐based conflict predictions ( p ≤ 0.05 for 13 out of 15 sub‐RQs). These findings provide stakeholders with actionable insights to inform decision‐making and requirement prioritization. Conclusion This paper addresses a significant gap in requirements engineering by introducing the first NLP‐augmented pairwise comparison method natively aligned with the Jackson problem frame. Unlike existing approaches, E‐PCM combines entity extraction, semantic parsing rules, and empirical conflict scaling to provide a domain‐independent, scalable, and data‐driven conflict identification framework acknowledged both in the literature and by practitioners.

In software development, requirements analysis plays a crucial role in the success of the project.Problem Frames (PF), a mainstream requirements analysis methodology, utilizes problem diagrams (PD) through modeling for more precise, coherent, and verifiable requirements analysis.However, the current problem diagram as a PF visualization tool is rather abstract and often suffers from node overlap and edge crossover, and such 2D diagrams limit the visualization and understanding of software requirements.In this paper, we present XR4PD, a novel prototype implementation for visualizing software requirement problem diagrams in 3D space using augmented reality.This immersive approach visualizes the structure of the scenarios implemented by the software system through interaction with the environment, and integrates domain-specific entity models with the interactive capabilities of refined problem diagrams, aiming to facilitate the understanding and analysis of software requirements.Feedback from the conducted assessment was positive.

Natural Language Processing (NLP) aims to study various theories and methods that enable effective communication between humans and computers in natural language. One specific technique, known as Keyphrase Extraction (KPE), has achieved significant success in recent years through pre-trained Language Models (LM), particularly BERT and ELMo. Currently, researchers have presented NLP4RE at Requirements Engineering (RE) conferences, contemplating how to leverage the cutting-edge advancements in NLP to achieve the integration of closely related research domains. In the practice of requirements engineering, it is not always assumed that the initial requirements description is complete, which can lead to requirements missing or changes. To address this issue, this paper proposes an unsupervised keyword extraction modeling method. Specifically, using the problem frames model as a case study, this method is integrated into the team's development of an iOS-based Problem Frames (PF) modeling tool in the form of an assisting dropdown list. It is linked with external knowledge bases to predict new keywords. We compare five unsupervised keyword extraction techniques with different principles and evaluate them using samples from the requirements engineering domain. In addition, an eye movement experiment is conducted to further assess the proposed method.

Requirements include the core functionalities of the software, however, due to their typical representations in natural language, professionals invest considerable effort in modeling to achieve more precise, coherent, and verifiable requirements analysis. This paper proposes a method called NL2PD for Problem Frames approach to extract requirements entities and relationships from requirements texts described in natural language and generate a problem diagrams to assist software requirements analysis. Given a requirements document in natural language, we can automatically extract required information and generate problem diagrams, on top of which users can edit, import and export. The method was tested with common requirements formats, demonstrating its ability to streamline the process of constructing problem diagrams from requirements. A demo video of this tool is available at https://youtu.be/SxoTikU1Mek.

Automatic extraction of non-functional requirements plays a crucial role in improving efficiency of requirements elicitation, change management, and validation testing. In recent years, machine learning methods have been widely applied in the field of requirements engineering. Although these methods have achieved some promising results, many of them have been evaluated only on small-scale and relatively balanced datasets of non-functional requirements, which may not reflect their typical characteristics in real-world applications. Therefore, in this paper, we propose a novel deep neural network model called NFRNet-LT to address the challenges of extracting non-functional requirements from various types of documents, by considering the higher granularity of non-functional requirement categories and the imbalanced long-tail distribution of data.

Microservices have an important position in today's software development, enabling a highly cohesive and low-coupling way of service organization. To address the complexity issues of inter-service communication, data consistency and distributed system management in the microservice decomposition process, we extend the traditional problem diagram based on the commonality of microservice decomposition and problem frames: the causal domain in the problem domain is further divided into communication components and business components. We design a microservice decomposition tool PF4MD to visualize service requirements and design problem decomposition and complexity calculation rules. We evaluated it with cases such as the Smart Building system and obtained a strategy similar to manual decomposition but with more intuitive and finer granularity, thus helping architects to make more efficient decisions and understand the decomposition process more clearly in system requirements.

This paper provides a support tool for Jackson's Problem Frames approach -named Trace4PF for decomposing a global problem diagram into sub-problem diagrams.The tool provides a web browser interface with features such as drawing, editing and performing syntactical checking, and highlighting the trace of causal chain.A video demonstration of the tool is available at https : //youtu.be/XSU V GGqEKkw.

Problem analysis has long been considered the key to requirements engineering, and the Problem Frames (PF) approach provides a structured method by deploying a common model for analyzing various types of problems. Problem decomposition is an important technique in structuring the software solution and also the key to reducing problem size and complexity. However, there has not been a suite of flexible and effective tools to describe details of problem domains in PF models. In this paper, we combine model-driven engineering and PF to provide a tool that can refine domain descriptions. In order to support modeling between domain stakeholders and software designers, we provide a technique and tool to allow the modeller to zoom in the details of a problem diagram, by adding UML State Machine Diagrams and SysML Block Definition Diagrams to domain descriptions.A demo video of this tool is available at https://youtu.be/BcQPlDYiOa8. More details of this tool and the appendix to this article are available at https://github.com/Wsfff-lf/ZOOM4PF/tree/main.