Software, Volume 4, Issue 2 (June 2025) – 7 articles

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