Decision Intelligence Methodology for AI-Driven Agile Software Lifecycle Governance and Architecture-Centered Project Management
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V4I1P112Keywords:
Decision Intelligence, Agile Software Development, Artificial Intelligence, Software Architecture Governance, Project ManagementAbstract
The integration of Artificial Intelligence (AI) in Agile Software Development methodologies has changed the way we make decisions in a project lifecycle that has led to better governance and more architecture-centric management strategies. In this study, the role of decision intelligence frameworks in agile environments is explored, where AI-driven systems are employed to optimize resource allocation, risk mitigation, and architectural decisions at stages of software development. A mixed-method approach was used for the research in which, for quantitative analysis, 350 software projects conducted in IT organizations from India are analyzed from 2020–2022, whereas qualitative information is obtained from the project managers who used the AI-based decision support systems. Results show that By comparison with traditional approaches, AI-augmented agile frameworks increased project success rates by 42%, decreased decision-making time by 58%, and improved resource optimization by 36%. Our statistical analysis show that AI adoption levels in companies are tightly correlated with project outcome measures, such as on-time delivery (r=0.78, p<0.01), budget (r=0.72, p<0.01) and quality measures (r=0.81, p<0.01). Our research provides an end-to-end decision intelligence framework that brings together AI methods and techniques with agile practices and provides actionable insights for professionals in software engineering to adopt intelligent systems to achieve better governance results
References
[1] Amershi, S., Begel, A., Bird, C., DeLine, R., Gall, H., Kamar, E., Nagappan, N., Nushi, B., & Zimmermann, T. (2019). Software engineering for machine learning: A case study. In International Conference on Software Engineering: Software Engineering in Practice (pp. 291-300). IEEE/ACM. https://doi.org/10.1109/ICSE-SEIP.2019.00042
[2] Enholm, I. M., Papagiannidis, E., Mikalef, P., & Krogstie, J. (2022). Artificial intelligence and business value: A literature review. Information Systems Frontiers, 24(5), 1709-1734. https://doi.org/10.1007/s10796-021-10186-w
[3] Eramo, R., Muttillo, V., Berardinelli, L., Bruneliere, H., Gomez, A., Bagnato, A., Sadovykh, A., & Cicchetti, A. (2022). AIDOaRt: AI-augmented automation for DevOps, a model-based framework for continuous development in Cyber-Physical Systems. Microprocessors and Microsystems, 94, 104672. https://doi.org/10.1016/j.micpro.2022.104672
[4] Fridgeirsson, T. V., Ingason, H. T., Jonasson, H. I., & Jonsdottir, H. (2021). An authoritative study on the near future effect of artificial intelligence on project management knowledge areas. Sustainability, 13(4), 2345. https://doi.org/10.3390/su13042345
[5] Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), 255-260. https://doi.org/10.1126/science.aaa8415
[6] Lewis, G. A., Ozkaya, I., & Xu, X. (2021). Software architecture challenges for ML systems. In International Conference on Software Maintenance and Evolution (pp. 634-638). IEEE. https://doi.org/10.1109/ICSME52107.2021.00071
[7] Lwakatare, L. E., Raj, A., Bosch, J., Olsson, H. H., & Crnkovic, I. (2019). A taxonomy of software engineering challenges for machine learning systems: An empirical investigation. In Agile Processes in Software Engineering and Extreme Programming (pp. 227-243). Springer. https://doi.org/10.1007/978-3-030-19034-7_14
[8] Meske, C., Bunde, E., Schneider, J., & Gersch, M. (2022). Explainable artificial intelligence: Objectives, stakeholders, and future research opportunities. Information Systems Management, 39(1), 53-63. https://doi.org/10.1080/10580530.2020.1849465
[9] Muccini, H., & Vaidhyanathan, K. (2021). Software architecture for ML-based systems: What exists and what lies ahead. In Workshop on AI Engineering Software Engineering for AI (pp. 121-128). IEEE/ACM. https://doi.org/10.1109/WAIN52551.2021.00026
[10] Quintana, M. A., Palacio, R. R., Soto, G. B., & González-López, S. (2022). Agile development methodologies and natural language processing: A mapping review. Computers, 11(12), 179. https://doi.org/10.3390/computers11120179
[11] Ribeiro, J., Lima, R., Eckhardt, T., & Paiva, S. (2021). Robotic process automation and artificial intelligence in industry 4.0—A literature review. Procedia Computer Science, 181, 51-58. https://doi.org/10.1016/j.procs.2021.01.104
[12] Schneider, J., Abraham, R., Meske, C., & Vom Brocke, J. (2022). Artificial intelligence governance for businesses. Information Systems Management, 39(3), 229-249. https://doi.org/10.1080/10580530.2022.2085825
[13] Kacheru, G., Bajjuru, R., & Arthan, N. (2019). Security Considerations When Automating Software Development. Revista de Inteligencia Artificial en Medicina, 10(1), 598-617
[14] Serban, A., & Visser, J. (2022). Adapting software architectures to machine learning challenges. In International Conference on Software Analysis, Evolution and Reengineering (pp. 152-163). IEEE. https://doi.org/10.1109/SANER53432.2022.00029
