Cross-functional teams are a central organizational model in contemporary software engineering fueled by practices such as Agile development, DevOps and Continuous Integration/Continuous Deployment (CI/CD). Although widely adopted, there is limited empirical support for the effectiveness of these practices in cross-functional teams. This paper presents evidence based software engineering for improving the performance of cross-functional teams. We propose a structured optimization framework which connects modern software engineering principles to measurable performance incentives. The framework is empirically validated on data from real cross-functional software teams, including delivery, quality and collaboration metrics. We show empirical evidence that we have increased deployment frequency, reduced time to release cycle, decreased defect density, improved recovery efficiency, and enhanced collaboration across teams through data-driven optimization. The results reveal that systematic and sustainable performance improvements are achieved through data driven decision making. The contributions of this paper move along the evidence-based software engineering and propose a theoretical and empirically grounded support for optimizing cross-functional software teams.
M. Qasim, Ali Zamil Sharhan Al-Maliki, Murtadha Al-Maliki et al.· 2026 6th International Confe...· 0 citations
Refactoring is widely used to improve internal software quality; however, its impact on external functionality remains insufficiently explored. This study investigates how different refactoring operations influence software functionality through a controlled experimental analysis. A set of ten commonly used refactoring operations was applied to the jEdit system. Software functionality was quantitatively evaluated using a composite metric derived from cohesion, polymorphism, interface size, design size, and inheritance hierarchy. The selected ten refactoring operations were performed 453 times across ten independent experiments in jEdit. The results reveal that refactoring operations do not have uniform effects on functionality. Operations that enhance encapsulation and modular distribution significantly improve functionality, with Encapsulate Field achieving the highest increase. In contrast, operations that reduce abstraction, such as Inline Method and Inline Class, negatively impact functionality. Additionally, some operations show no measurable effect, indicating limitations in metric sensitivity. These findings demonstrate that refactoring should not be assumed to universally improve software functionality. Instead, its impact depends on the nature and context of the operations being applied. The study provides empirical evidence and practical guidance for selecting refactoring operations that effectively enhance functional quality while avoiding potential degradation.
Abdullah Almogahed, Manal Othman, M. Qasim et al.· 2026 6th International Confe...· 0 citations
This study collected literature from major databases and revealed that all researchers addressed security aspects, such as basic authentication and privacy, but they neglected cryptographic measures, which remain the weakest point of execution in Arduino-based medical systems.
M. Qasim, Murtadha Al-Maliki, Radhwan Sneesl et al.· Applied computing Journal· 0 citations
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