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

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29 pages, 6806 KiB  
Article
Enhancing DevOps Practices in the IoT–Edge–Cloud Continuum: Architecture, Integration, and Software Orchestration Demonstrated in the COGNIFOG Framework
by Kostas Petrakis, Evangelos Agorogiannis, Grigorios Antonopoulos, Themistoklis Anagnostopoulos, Nasos Grigoropoulos, Eleni Veroni, Alexandre Berne, Selma Azaiez, Zakaria Benomar, Harry Kakoulidis, Marios Prasinos, Philippos Sotiriades, Panagiotis Mavrothalassitis and Kosmas Alexopoulos
Software 2025, 4(2), 10; https://doi.org/10.3390/software4020010 - 15 Apr 2025
Viewed by 488
Abstract
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 [...] Read more.
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
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19 pages, 1575 KiB  
Article
Regression Testing in Agile—A Systematic Mapping Study
by Suddhasvatta Das and Kevin Gary
Software 2025, 4(2), 9; https://doi.org/10.3390/software4020009 - 14 Apr 2025
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Abstract
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 [...] Read more.
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
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16 pages, 2662 KiB  
Article
Uplifting Moods: Augmented Reality-Based Gamified Mood Intervention App with Attention Bias Modification
by Yun Jung Yeh, Sarah S. Jo and Youngjun Cho
Software 2025, 4(2), 8; https://doi.org/10.3390/software4020008 - 1 Apr 2025
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Abstract
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 [...] Read more.
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
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34 pages, 2285 KiB  
Article
Empirical Analysis of Data Sampling-Based Decision Forest Classifiers for Software Defect Prediction
by Fatima Enehezei Usman-Hamza, Abdullateef Oluwagbemiga Balogun, Hussaini Mamman, Luiz Fernando Capretz, Shuib Basri, Rafiat Ajibade Oyekunle, Hammed Adeleye Mojeed and Abimbola Ganiyat Akintola
Software 2025, 4(2), 7; https://doi.org/10.3390/software4020007 - 21 Mar 2025
Viewed by 305
Abstract
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 [...] Read more.
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
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