Software

This study investigates how Development and Operations (DevOps) practices impact Project Management Office (PMO) governance within the technology sector of the United Arab Emirates (UAE). It addresses the need for agile-aligned governance frameworks by exploring how DevOps principles affect traditional PMO structures. A quantitative cross-sectional survey was conducted, and data was collected from 321 DevOps and PMO professionals in UAE organizations. The analysis, using Partial Least Squares Structural Equation Modelling (PLS-SEM), revealed a moderate positive correlation between specific DevOps practices—such as microservices, Minimum Viable Experience (MVE) culture, continuous value streams, automated configuration, and continuous delivery—and effective PMO governance. The study’s novel theoretical contribution is the integration of the Dynamic Capabilities Framework (DCF) with the Agile DevOps Reference Model (ADRM) to examine this alignment, bridging strategic agility and operational execution. This research offers actionable insights for UAE organizations and policymakers seeking to enhance governance and digital maturity. Full article

This paper introduces a variant of the classic Towers of Hanoi (TH) puzzle in which parallel moves—simultaneous transfers of multiple discs—are permitted. The problem is formalized with Generalized Nets (GN), an extension of Petri nets (PN) whose tokens and transitions encode the ordering and movement of the n discs among three rods under the usual constraints. The resulting GN model, implemented on the OnlineGN platform, provides a clear, precise, and systematic representation that highlights the role of parallelism and supports interactive experimentation. This framework enables the exploration of strategies that reduce the number of parallel steps (PS) and, more broadly, illustrates how GN-captured parallelism can shorten the sequential depth for selected problems with exponential-time solutions. Full article

Context: Modern software systems have become increasingly complex, making it difficult to interpret raw requirements and effectively utilize traditional tools for software design and analysis. Unified Modeling Language (UML) class diagrams are widely used to visualize and understand system architecture, but analyzing them manually, especially for large-scale systems, poses significant challenges. Objectives: This study aims to automate the analysis of UML class diagrams by assessing their complexity using a machine learning approach. The goal is to support software developers in identifying potential design issues early in the development process and to improve overall software quality. Methodology: To achieve this, this research introduces a Visual Question Answering (VQA)-based framework that integrates both computer vision and natural language processing. Vision Transformers (ViTs) are employed to extract global visual features from UML class diagrams, while the BERT language model processes natural language queries. By combining these two models, the system can accurately respond to questions related to software complexity, such as class coupling and inheritance depth. Results: The proposed method demonstrated strong performance in experimental trials. The ViT model achieved an accuracy of 0.8800, with both the F1 score and recall reaching 0.8985. These metrics highlight the effectiveness of the approach in automatically evaluating UML class diagrams. Conclusions: The findings confirm that advanced machine learning techniques can be successfully applied to automate software design analysis. This approach can help developers detect design flaws early and enhance software maintainability. Future work will explore advanced fusion strategies, novel data augmentation techniques, and lightweight model adaptations suitable for environments with limited computational resources. Full article

The development of software applications using multiple programming languages has increased in recent years, as it allows the selection of the most suitable language and runtime for each component of the system and the integration of third-party libraries. However, this practice involves complexity and error proneness, due to the absence of an adequate system for the interoperability of multiple programming languages. Developers are compelled to resort to workarounds, such as library reimplementation or language-specific wrappers, which are often dependent on C as the common denominator for interoperability. These challenges render the use of multiple programming languages a burdensome and demanding task that necessitates highly skilled developers for implementation, debugging, and maintenance, and raise doubts about the benefits of interoperability. To overcome these challenges, we propose MetaFFI, introducing a fully in-process, plugin-oriented, runtime-independent architecture based on a minimal C abstraction layer. It provides deep binding without relying on a shared object model, virtual machine bytecode, or manual glue code. This architecture is scalable (O(n) integration for n languages) and supports true polymorphic function and object invocation across languages. MetaFFI is based on leveraging FFI and embedding mechanisms, which minimize restrictions on language selection while still enabling full-duplex binding and deep integration. This is achieved by exploiting the less restrictive shallow binding mechanisms (e.g., Foreign Function Interface) to offer deep binding features (e.g., object creation, methods, fields). MetaFFI provides a runtime-independent framework to load and xcall (Cross-Call) foreign entities (e.g., getters, functions, objects). MetaFFI uses Common Data Types (CDTs) to pass parameters and return values, including objects and complex types, and even cross-language callbacks and dynamic calling conventions for optimization. The indirect interoperability approach of MetaFFI has the significant advantage of requiring only $2n$ mechanisms to support n languages, compared to direct interoperability approaches that need $n^2$ mechanisms. We developed and tested a proof of concept tool interoperating three languages (Go, Python, and Java), on Windows and Ubuntu. To evaluate the approach and the tool, we conducted a user study, with promising results. The MetaFFI framework is available as open source software, including its full source code and installers, to facilitate adoption and collaboration across academic and industrial communities. Full article

Modern web applications increasingly demand rendering techniques that optimize performance, responsiveness, and scalability. Progressive Server-Side Rendering (PSSR) bridges the gap between Server-Side Rendering and Client-Side Rendering by progressively streaming HTML content, improving perceived load times. Still, traditional HTML template engines often rely on blocking interfaces that hinder their use in asynchronous, non-blocking contexts required for PSSR. This paper analyzes how Java virtual threads, introduced in Java 21, enable non-blocking execution of blocking I/O operations, allowing the reuse of traditional template engines for PSSR without complex asynchronous programming models. We benchmark multiple engines across Spring WebFlux, Spring MVC, and Quarkus using reactive, suspendable, and virtual thread-based approaches. Results show that virtual threads allow blocking engines to scale comparably to those designed for non-blocking I/O, achieving high throughput and responsiveness under load. This demonstrates that virtual threads provide a compelling path to simplify the implementation of PSSR with familiar HTML templates, significantly lowering the barrier to entry while maintaining performance. Full article

Background: While several test-automation maturity models (e.g., CMMI, TMMi, TAIM) exist, none explicitly integrate ISO 9001-based quality management systems (QMS), leaving a gap for organizations that must align E2E test automation with formal quality assurance. Objective: This study proposes a test-automation maturity model (TAMM) that bridges E2E automation capability with ISO 9001/ISO 9004 self-assessment principles, and evaluates its reliability and practical impact in industry. Methods: TAMM comprises eight maturity dimensions, 39 requirements, and 429 checklist items. Three independent assessors applied the checklist to three software teams; inter-rater reliability was ensured via consensus review (Cohen’s κ = 0.75). Short-term remediation actions based on the checklist were implemented over six months and re-assessed. Synergy with the organization’s ISO 9001 QMS was analyzed using ISO 9004 self-check scores. Results: Within 6 months of remediation, mean TAMM score rose from 2.75 → 2.85. Inter-rater reliability is filled with Cohen’s κ = 0.75. Conclusions: The proposed TAMM delivers measurable, short-term maturity gains and complements ISO 9001-based QMS without introducing conflicting processes. Practitioners can use the checklist to identify actionable gaps, prioritize remediation, and quantify progress, while researchers may extend TAMM to other domains or automate scoring via repository mining. Full article

Intersectionality is a concept used to explain the power dynamics and inequalities that some groups experience owing to the interconnection of social differences such as in gender, sexual identity, poverty status, race, geographic location, disability, and education. The relation between software engineering, feminism, and intersectionality has been addressed by some studies thus far, but it has never been codified before. In this paper, we employ the commonly used ABC Framework for empirical software engineering to show the contributions of intersectional software engineering (ISE) as a field of software engineering. In addition, we highlight the power dynamic, unique to ISE studies, and define gender-forward intersectionality as a way to use gender as a starting point to identify and examine inequalities and discrimination. We show that ISE is a field of study in software engineering that uses gender-forward intersectionality to produce knowledge about power dynamics in software engineering in its specific domains and environments. Employing empirical software engineering research strategies, we explain the importance of recognizing and evaluating ISE through four dimensions of dynamics, which are people, processes, products, and policies. Beginning with a set of 10 seminal papers that enable us to define the initial concepts and the query for the systematic mapping study, we conduct a systematic mapping study leads to a dataset of 140 primary papers, of which 15 are chosen as example papers. We apply the principles of ISE to these example papers to show how the field functions. Finally, we conclude the paper by advocating the recognition of ISE as a specialized field of study in software engineering. Full article

Benchmark results for large language models often show inconsistencies across different studies. This paper investigates the challenges of reproducing these results in automatic bugfixing using LLMs, on the HumanEvalFix benchmark. To determine the cause of the differing results in the literature, we attempted to reproduce a subset of them by evaluating 12 models in the DeepSeekCoder, CodeGemma, CodeLlama, and WizardCoder model families, in different sizes and tunings. A total of 35 unique results were reported for these models across studies, of which we successfully reproduced 12. We identified several relevant factors that influenced the results. The base models can be confused with their instruction-tuned variants, making their results better than expected. Incorrect prompt templates or generation length can decrease benchmark performance, as well as using 4-bit quantization. Using sampling instead of greedy decoding can increase the variance, especially with higher temperature values. We found that precision and 8-bit quantization have less influence on benchmark results. Full article

In this paper we examine the OpenJDK library implementation of the ConcurrentLinkedQueue. We use model checking to verify that it behaves according to the algorithm it is based on: Michael and Scott’s fast and practical non-blocking concurrent queue algorithm. In addition, we develop a simple concurrent queue specification in CSP and verify that Michael and Scott’s algorithm satisfies it. We conclude that both the algorithm and the implementation are correct and both conform to our simpler concurrent queue specification, which we can use in place of either implementation in future verification tasks. The complete code is available on GitHub. Full article

Software requirements engineering is one of the most critical and time-consuming phases of the software-development process. The lack of communication with stakeholders and the use of natural language for communicating leads to misunderstanding and misidentification of requirements or the creation of ambiguous requirements, which can jeopardize all subsequent steps in the software-development process and can compromise the quality of the final software product. Natural Language Processing (NLP) is an old area of research; however, it is currently undergoing strong and very positive impacts with recent advances in the area of Machine Learning (ML), namely with the emergence of Deep Learning and, more recently, with the so-called transformer models such as BERT and GPT. Software requirements engineering is also being strongly affected by the entire evolution of ML and other areas of Artificial Intelligence (AI). In this article we conduct a systematic review on how AI, ML and NLP are being used in the various stages of requirements engineering, including requirements elicitation, specification, classification, prioritization, requirements management, requirements traceability, etc. Furthermore, we identify which algorithms are most used in each of these stages, uncover challenges and open problems and suggest future research directions. Full article

This study investigates the prevalence and impact of Agile practices by leveraging metadata from thousands of public GitHub repositories through a novel data-driven methodology. To facilitate this analysis, we developed the AgileScore index, a metric designed to identify and evaluate patterns, characteristics, performance and community engagement in Agile-oriented projects. This approach enables comprehensive, large-scale comparisons between Agile methodologies and traditional development practices within digital environments. Our findings reveal a significant annual growth of 16% in the adoption of Agile practices and validate the AgileScore index as a systematic tool for assessing Agile methodologies across diverse development contexts. Furthermore, this study introduces innovative analytical tools for researchers in software project management, software engineering and related fields, providing a foundation for future work in areas such as cost estimation and hybrid project management. These insights contribute to a deeper understanding of Agile’s role in fostering collaboration and adaptability in dynamic digital ecosystems. Full article

The decision tree test method works as a flowchart structure for conversational flow. It has predetermined questions and answers that guide the user through specific tasks. Inspired by principles of the decision tree test method in software engineering, this paper discusses intelligent AI test modeling chat systems, including basic concepts, quality validation, test generation and augmentation, testing scopes, approaches, and needs. The paper’s novelty lies in an intelligent AI test modeling chatbot system built and implemented based on an innovative 3-dimensional AI test model for AI-powered functions in intelligent mobile apps to support model-based AI function testing, test data generation, and adequate test coverage result analysis. As a result, a case study is provided using a mental health and emotional intelligence chatbot system, Wysa. It helps in tracking and analyzing mood and helps in sentiment analysis. Full article

Satellite and edge computing designers develop algorithms that restrict resource utilization and execution time. Among these design efforts, optimizing Fast Fourier Transform (FFT), key to many tasks, has led mainly to in-place FFT-specific hardware accelerators. Aiming at improving the FFT performance on processors and computing devices with limited resources, the current paper enhances the efficiency of the radix-2 FFT by exploring the benefits of an in-place technique. First, we present the advantages of organizing the single memory bank of processors to store two (2) FFT elements in each memory address and provide parallel load and store of each FFT pair of data. Second, we optimize the floating point (FP) and block floating point (BFP) configurations to improve the FFT Signal-to-Noise (SNR) performance and the resource utilization. The resulting techniques reduce the memory requirements by two and significantly improve the time performance for the overall prevailing BFP representation. The execution of inputs ranging from 1K to 16K FFT points, using 8-bit or 16-bit as FP or BFP numbers, on the space-proven Atmel AVR32 and Vision Processing Unit (VPU) Intel Movidius Myriad 2, the edge device Raspberry Pi Zero 2W and a low-cost accelerator on Xilinx Zynq 7000 Field Programmable Gate Array (FPGA), validates the method’s performance improvement. Full article

This paper presents COGNIFOG, an innovative framework under development that is designed to leverage decentralized decision-making, machine learning, and distributed computing to enable autonomous operation, adaptability, and scalability across the IoT–edge–cloud continuum. The work emphasizes Continuous Integration/Continuous Deployment (CI/CD) practices, development, and versatile integration infrastructures. The described methodology ensures efficient, reliable, and seamless integration of the framework, offering valuable insights into integration design, data flow, and the incorporation of cutting-edge technologies. Through three real-world trials in smart cities, e-health, and smart manufacturing and the development of a comprehensive QuickStart Guide for deployment, this work highlights the efficiency and adaptability of the COGNIFOG platform, presenting a robust solution for addressing the complexities of next-generation computing environments. Full article

Background: Regression testing is critical in agile software development, as it ensures that frequent changes do not introduce defects into previously working functionalities. While agile methodologies emphasize rapid iterations and value delivery, regression testing research has predominantly focused on optimizing technical efficiency rather than aligning with agile principles. Aim: This study aims to systematically map research trends and gaps in regression testing within agile environments, identifying areas that require further exploration to enhance alignment with agile practices and value-driven outcomes. Method: A systematic mapping study analyzed 35 primary studies. The research categorized studies based on their focus areas, evaluation metrics, agile frameworks, and methodologies, providing a comprehensive overview of the field. Results: The findings strongly emphasize test prioritization and selection, reflecting the need for optimized fault detection and execution efficiency in agile workflows. However, areas such as test generation, test minimization, and cost analysis are under-explored. Current evaluation metrics primarily address technical outcomes, neglecting agile-specific aspects like defect severity’s business impact and iterative workflows. Additionally, the research highlights the dominance of continuous integration frameworks, with limited attention to other agile practices like Scrum and a lack of datasets capturing agile-specific attributes such as testing costs and user story importance. Conclusions: This study underscores the need for research to expand beyond existing focus areas, exploring diverse testing techniques and developing agile-centric metrics and datasets. By addressing these gaps, future work can enhance the applicability of regression testing strategies and align them more closely with agile development principles. Full article

Attention Bias Modification (ABM) is a cost-effective mood intervention that has the potential to be used in daily settings beyond clinical environments. However, its interactivity and user engagement are known to be limited and underexplored. Here, we propose Uplifting Moods, a novel mood intervention app that combines gamified ABM and augmented reality (AR) to address the limitation associated with the repetitive nature of ABM. By harnessing the benefits of mobile AR’s low-cost, portable, and accessible characteristics, this approach is to help users easily take part in ABM, positively shifting one’s emotions. We conducted a mixed methods study with 24 participants, which involves a controlled experiment with Self-Assessment Manikin as its primary measure and a semi-structured interview. Our analysis reports that the approach uniquely adds fun, exploring, and challenging features, helping improve engagement and feeling more cheerful and less under control. It also highlights the importance of personalization and consideration of gaming style, music preference, and socialization in designing a daily AR ABM game as an effective mental wellbeing intervention. Full article

The strategic significance of software testing in ensuring the success of software development projects is paramount. Comprehensive testing, conducted early and consistently across the development lifecycle, is vital for mitigating defects, especially given the constraints on time, budget, and other resources often faced by development teams. Software defect prediction (SDP) serves as a proactive approach to identifying software components that are most likely to be defective. By predicting these high-risk modules, teams can prioritize thorough testing and inspection, thereby preventing defects from escalating to later stages where resolution becomes more resource intensive. SDP models must be continuously refined to improve predictive accuracy and performance. This involves integrating clean and preprocessed datasets, leveraging advanced machine learning (ML) methods, and optimizing key metrics. Statistical-based and traditional ML approaches have been widely explored for SDP. However, statistical-based models often struggle with scalability and robustness, while conventional ML models face challenges with imbalanced datasets, limiting their prediction efficacy. In this study, innovative decision forest (DF) models were developed to address these limitations. Specifically, this study evaluates the cost-sensitive forest (CS-Forest), forest penalizing attributes (FPA), and functional trees (FT) as DF models. These models were further enhanced using homogeneous ensemble techniques, such as bagging and boosting techniques. The experimental analysis on benchmark SDP datasets demonstrates that the proposed DF models effectively handle class imbalance, accurately distinguishing between defective and non-defective modules. Compared to baseline and state-of-the-art ML and deep learning (DL) methods, the suggested DF models exhibit superior prediction performance and offer scalable solutions for SDP. Consequently, the application of DF-based models is recommended for advancing defect prediction in software engineering and similar ML domains. Full article

Microservices architecture has emerged as a dominant approach for developing scalable and modular software systems, driven by the need for agility and independent deployability. However, designing these architectures poses significant challenges, particularly in service decomposition, inter-service communication, and maintaining data consistency. To address these issues, artificial intelligence (AI) techniques, such as machine learning (ML) and natural language processing (NLP), have been applied with increasing frequency to automate and enhance the design process. This systematic literature review examines the application of AI in microservices design, focusing on AI-driven tools and methods for improving service decomposition, decision-making, and architectural validation. This review analyzes research studies published between 2018 and 2024 that specifically focus on the application of AI techniques in microservices design, identifying key AI methods used, challenges encountered in integrating AI into microservices, and the emerging trends in this research area. The findings reveal that AI has effectively been used to optimize performance, automate design tasks, and mitigate some of the complexities inherent in microservices architectures. However, gaps remain in areas such as distributed transactions and security. The study concludes that while AI offers promising solutions, further empirical research is needed to refine AI’s role in microservices design and address the remaining challenges. Full article