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.

ABSTRACT Enterprise Information System (EIS) streamlines business processes and enhances productivity by integrating various functions. However, conventional development methods are labor‐intensive, time‐consuming, and error‐prone, often necessitating a design model from requirements for implementation. Existing solutions focus on auto‐generating code from Object‐Oriented (OO) design models, but specifying the design model from a validated requirements model requires more effort due to information gaps between requirements and design. This paper introduces RM2EIS, an approach that automatically generates EIS from contract‐based requirements models, which include use case diagrams, conceptual class diagrams, and use case definitions specified by system sequence diagrams and contracts. System operation contracts are formally specified using pre‐ and post‐conditions written in OCL. We conducted nine case studies to evaluate RM2EIS. The results indicate that the time of the generation including modeling and validation by RM2EIS is at least twice as fast as the design and implementation of developers. Moreover, the generated EIS outperforms the developer‐implemented systems in functionality and is close to the non‐functional aspects like performance.

The current popular two-stream two-stage image fusion framework extracts features of infrared and visible images separately and then performs feature fusion. The extracted features lack interaction between the source images and have limited cross-modal complementary capability. To address these issues, we propose a novel one-stream infrared and visible image fusion (OSFusion) framework that connects a source image pair to achieve bidirectional information flow. In this way, the fused features with cross-modal complementary information can be dynamically extracted by mutual guidance. To further improve the inference efficiency and obtain high-quality fused images, a feature extraction and fusion module (FEFM) is proposed based on Transformer structure. The combination of feature extraction and feature fusion is realized by using it. Since there is no need for an extra feature interaction module and the implementation is highly parallel, the speed of image fusion is extremely fast. Benefiting from the one-stream structure and FEFM, OSFusion achieves promising infrared and visible image fusion performance on MSRS, M3FD, and RoadScene datasets. Besides, our method achieves a good balance in the trade-off between performance and complexity, and also shows a faster convergence trend.

Enterprise information systems(EIS) focus on dealing with the complex business logic of collecting, filtering, processing, and distributing data for improving productivity and service in our daily life. The successful development of enterprise information system is the labor-intensive activities in software engineering, it requires the sophisticated human efforts for requirements validation, system design, implementation and verification. Our previous work RM2PT can help to achieve a validated requirements model through automatically generating prototypes from requirements models to support incremental and rapid requirements validation. In this paper, we present a tool named RM2EIS to further alleviate the problem of system development by supporting automatically generate the back-end source code of enterprise information system from the validated requirements model, which are achieved by several round requirements validation in RM2PT. We demonstrate that RM2EIS can achieve higher quality code (+8.27%) with less time cost (-61.92%) than the manual development through 9 case studies. Overall, the results were satisfactory. The proposed approach can be further extended and applied for the EIS development in the software industry. The tool can be downloaded at https://rm2pt.com/advs/rm2eis and a demo video casting its features is available at https://www.youtube.com/watch?v=5Nde-JYezg4.

With the progressive augmentation of the density of 3-D processor arrays, some processor elements (PEs) often fail due to overload or overheating during massively parallel computing operations. Therefore, it is necessary to take effective fault-tolerant technology to ensure the reliability of the system. This article investigates an efficient reconfiguration method to construct 3-D fault-free logical subarray with more fault-free PEs and less interconnection length (interlength). First, we propose a novel method based on the barrel effect to find the bottleneck plane of 3-D processor arrays. Second, an efficient compensation strategy is proposed to replace faulty PEs on adjacent physical planes with fault-free PEs on the bottleneck planes, which leads to more fault-free PEs that can be used to construct the subarray. Then, we propose a heuristic to construct the subarray and optimize iteration redundancy to accelerate reconstruction. Finally, a heuristic optimization algorithm is proposed to reduce the interlength between PEs, which can reduce the dynamic power consumption and communication costs. In addition, we propose a more accurate method to calculate the lower bound of the interlength to better evaluate the performance of the algorithm. Simulation experiments show that, compared to the state-of-the-arts, on$128\times 128\times 128$host array, the utilization rate of fault-free PEs can be improved up to 15.6% and the interlength redundancy can be reduced by 78.2% for random faults. On$64\times 64\times 64$host array, the average improvement of the two indicators under clustered faults can reach 93.2% and 69.3%. Moreover, for all cases considered, the proposed new lower bound and reconstruction time can be reduced by an average of 18.47% and 76.13%, respectively.

In [1, p. 4967], a figure citation is incorrect and “Fig. 3(c)” should be “Fig. 1(c)” in the left column, the fourth line from the bottom. It is corrected below.

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.

With the rapid development and widespread application of Knowledge graphs (KGs) in many artificial intelligence tasks, a large number of efforts have been made to refine them and increase their quality. Knowedge graph embedding (KGE) has become one of the main refinement tasks, which aims to predict missing facts based on existing ones in KGs. However, there are still mainly two difficult unresolved challenges: (i) how to leverage the local structural features of entities and the potential soft logical rules to learn more expressive embedding of entites and relations; and (ii) how to combine these two learning processes into one unified model. To conquer these problems, we propose a novel KGE model named JSSKGE, which can \textbf{J}ointly learn the local \textbf{S}tructural features of entities and \textbf{S}oft logical rules. Firstly, we employ graph attention networks which are specially designed for graph-structured data to aggregate the local structural information of nodes. Then, we utilize soft logical rules implicated in KGs as an expert to further rectify the embeddings of entities and relations. By jointly learning, we can obtain more informative embeddings to predict new facts. With experiments on four commonly used datasets, the JSSKGE obtains better performance than state-of-the-art approaches.

Code coverage has been widely used as the basis for various software quality assurance techniques. Therefore, it is of great importance to ensure that coverage profilers provide reliable code coverage. However, it is challenging to validate the correctness of the code coverage generated due to the lack of an effective oracle. In this paper, we propose an effective approach based on control flow constraint solving to test coverage profilers and have implemented a coverage bug hunting tool, DOG (finD cOverage buGs). Our core idea is to leverage inherent control flow features to generate control flow constraints that the resulting coverage statistics should respect. If DOG identifies any unsatisfiable constraints, it signifies the presence of incorrect coverage statistics. In such cases, DOG provides detailed diagnostic information about the suspicious coverage statistics for manual inspection. Compared with the state-of-the-art works, DOG has the following prominent advantages: (1) wide applicability: DOG eliminates the need for multiple coverage profilers (as required by differential testing) and program variants (as needed in metamorphic testing), making it highly versatile; (2) unique testing capability: DOG effectively analyzes and utilizes relationships among available coverage statistics, boosting its testing capabilities; and (3) enhanced interpretability: DOG provides clear control flow explanations for incorrect code coverage, enabling the localization of suspicious coverage areas. During our testing period with DOG, we successfully identified and reported 27 bugs in Gcov and llvm-cov, both widely-used coverage profilers. Of these, 17 bugs have been confirmed (11 have been fixed), 3 were deemed expected behaviors by developers, and 7 remain unresolved. Remarkably, 21 out of 24 unexpected bugs had been latent for over two and a half years, and nearly half of the coverage bugs (10 out of 24) were undetectable by state-of-the-art coverage profiler validators. These results demonstrate the effectiveness and importance of using DOG to improve the reliability of code coverage profilers.

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.

Current KeY verification tool for Java programs provides limited capability for verifying Java programs.In order to solve this problem, we provide a method for simplifying complex Java programs into a format that is compatible with the KeY.A set of simplification rules based on abstract syntax tree (AST) are proposed.These rules can keep the logic and semantics of the original Java programs mostly unchanged, while meeting the requirements of KeY verification tool.The paper concludes with a bank ATM example to demonstrate the feasibility of our work.

Non-functional requirements specify those qualities that software products must have in order to meet the user’s business requirements. The elicitation of these non-functional requirements requires expertise, experience, and domain knowledge, which is challenging and time-consuming for requirements engineers and developers. It would be very beneficial if the nonfunctional requirements can be automatically extracted from the requirements documentation to reduce the human efforts, time, and avoid the mental fatigue. In this paper, we present a novel deep neural network model called NFRNet to automatically extract non-functional requirements from software requirements documentation.

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.

Automated service classification plays a crucial role in service discovery, selection, and composition. Machine learning has been widely used for service classification in recent years. However, the performance of conventional machine learning methods highly depends on the quality of manual feature engineering. In this paper, we present a novel deep neural network to automatically abstract low-level representation of both service name and service description to high-level merged features without feature engineering and the length limitation, and then predict service classification on 50 service categories. To demonstrate the effectiveness of our approach, we conduct a comprehensive experimental study by comparing 10 machine learning methods on 10,000 real-world web services. The result shows that the proposed deep neural network can achieve higher accuracy in classification and more robust than other machine learning methods.

Context: Data synthesis is one of the most significant tasks in Systematic Literature Review (SLR). Software Engineering (SE) researchers have adopted a variety of methods of synthesizing data that originated in other disciplines. One of the qualitative data synthesis methods is meta-ethnography, which is being used in SE SLRs. Objective: We aim at studying the adoption of meta-ethnography in SE SLRs in order to understand how this method has been used in SE. Method: We conducted a tertiary study of the use of meta-ethnography by reviewing sixteen SLRs. We carried out an empirical inquiry by integrating SLR and confirmatory email survey. Results: There is a general lack of knowledge, or even awareness, of different aspects of meta-ethnography and/or how to apply it. Conclusion: There is a need of investment in gaining in-depth knowledge and skills of correctly applying meta-ethnography in order to increase the quality and reliability of the findings generated from SE SLRs. Our study reveals that meta-ethnography is a suitable method to SE research. We discuss challenges and propose recommendations of adopting meta-ethnography in SE. Our effort also offers a preliminary checklist of the systematic considerations for doing meta-ethnography in SE and improving the quality of meta-ethnographic research in SE.

Cyber-Physical Systems (CPSs) connect the cyber world with the physical world through a network of interrelated elements, such as sensors and actuators, robots, and other computing devices. There are increasing number of beneficial applications in dependable sectors such as aviation, transportation, aerospace, healthcare, etc.. The inherent characteristics of CPSs pose a number of challenges to requirements engineering. Unlike normal information systems, CPSs need to continuously detect and adapt to the environment changes. The interactive environment becomes the first-class citizen because the features and the changing patterns in environment are must-to-be considered. Moreover, in such systems, many non-functional requirements are environment related, like timing, safety, security, and privacy requirements. This tutorial will introduce an environment modelling based approach to engineering the requirements of CPSs. Extending the Problem Frames representations, this approach structures the model of the environmental elements and provides analysis methods for deriving and specifying requirements. We deliver this tutorial with a few supporting tools that assist the modelling and verification of the system specification, demonstrated with working examples in sufficient details. After the tutorial, participants will be able to work on the environment modelling requirements engineering for their own projects, with some hands-on experience and a good knowledge of some tool support.

This paper presents a set of computer-aided tools for problem analysis in the software development process. Jackson’s problem diagrams are used to model the problem owners’ needs and relevant contexts for the software to be built. An algorithm based on three classes of rules is provided for the systematic transformation of these models into behavioral descriptions of the software. This work is part of our long-term research efforts aiming at embedding and empirically evaluating Jackson’s Problem Frames framework (PF) in requirements engineering practice.

Background: One of the anticipated benefits of systematic literature reviews (SLRs) is that they can be conducted in an auditable way to produce repeatable results. Aim: This study aims to identify under what conditions SLRs are likely to be stable, with respect to the primary studies selected, when used in software engineering. The conditions we investigate in this report are when novice researchers undertake searches with a common goal. Method: We undertook a participant-observer multi-case study to investigate the repeatability of systematic literature reviews. The 'cases' in this study were the early stages, involving identification of relevant literature, of two SLRs of unit testing methods. The SLRs were performed independently by two novice researchers. The SLRs were restricted to the ACM and IEEE digital libraries for the years 1986-2005 so their results could be compared with a published expert literature review of unit testing papers. Results: The two SLRs selected very different papers with only six papers out of 32 in common, and both differed substantially from a published secondary study of unit testing papers finding only three of 21 papers. Of the 29 additional papers found by the novice researchers, only 10 were considered relevant. The 10 additional relevant papers would have had an impact on the results of the published study by adding three new categories to the framework and adding papers to three, otherwise empty, cells. Conclusions: In the case of novice researchers, having broadly the same research question will not necessarily guarantee repeatability with respect to primary studies. Systematic reviews must be careful to report their search process fully or they will not be repeatable. Missing papers can have a significant impact on the stability of the results of a secondary study.