Agile Software Engineering: Challenges and Best Practices for Scaling Agile in Large Organizations
Abstract
Agile methodologies have revolutionized the way software is developed, with a focus on flexibility, collaboration, and iterative delivery. However, scaling Agile practices in large organizations presents unique challenges related to team coordination, communication, and maintaining consistency across multiple teams. This paper investigates the key challenges faced when scaling Agile, including issues with organizational culture, resource management, and integration across different functional teams. Drawing from empirical studies and case examples, the paper proposes a set of best practices for scaling Agile, such as the use of frameworks like SAFe (Scaled Agile Framework) and LeSS (Large-Scale Scrum). The paper also discusses how large organizations can foster an Agile mindset and overcome the barriers to widespread adoption, thereby achieving the full benefits of Agile methodologies in a large-scale context.
References
Jurgovsky, J., Granitzer, M., Ziegler, K., Calabretto, S., Portier, P. E., He-Guelton, L., & Caelen, O. (2018). Sequence classification for credit-card fraud detection. Expert Systems with Applications, 100, 234–245.
Chen, C., & Chen, M. (2020). A hybrid deep learning model for detecting financial fraud. In Proceedings of the IEEE Symposium on Computers and Communications (pp. 345–350).
Whitrow, C., Hand, D. J., Juszczak, P., Weston, D., & Adams, N. M. (2009). Transaction aggregation as a strategy for credit card fraud detection. Data Mining and Knowledge Discovery, 18(1), 30–55.
Van Vlasselaer, V., Eliassi-Rad, T., Akoglu, L., Snoeck, M., & Baesens, B. (2017). GOTCHA! Network-based fraud detection for social security fraud. Management Science, 63(9), 3090–3110.
Cardozo, K., Nehmer, L., Esmat, Z. A. R. E., Afsari, M., Jain, J., Parpelli, V., ... & Shahid, T. (2024). U.S. Patent No. 11,893,819. Washington, DC: U.S. Patent and Trademark Office.
Gupta, M., & Jain, J. (2024). Optimizing E-commerce Dynamic Pricing Using Aggregated Market Data and Cloud-Based Analytics. International Journal of Global Innovations and Solutions (IJGIS).
Jain, J., & Gupta, M. (2024). Enhancing Software Engineering Practices for AI-Driven Fintech Applications. International Journal of Global Innovations and Solutions (IJGIS).
Jain, J. (2024). AI-Driven Optical Character Recognition for Fraud Detection in FinTech Income Verification Systems.
Cardozo, Kenneth, Landon Nehmer, Z. A. R. E. Esmat, Mani Afsari, Jitender Jain, Venkateshwar Parpelli, Bhuvaneswari Balasubramanian, Bijun Du, Daniel Nizinski, and Tausif Shahid. "Systems and methods for extracting and processing data using optical character recognition in real-time environments." U.S. Patent Application 18/429,247, filed May 23, 2024.
Jain, J. Leveraging Advanced AI and Cloud Computing for Scalable Innovations in Fintech Systems, 2022.
Jain, J., Khunger, A., Agarwal, G., Tanikonda, A., & Modake, R. (2021). Optimizing Payment Gateways in Fintech Using AI-Augmented OCR and Intelligent Workflow. Authorea Preprints.
Jain, J., Modake, R., Khunger, A., & dnyandev Jagdale, A. CLOUD-NATIVE SECURITY FRAMEWORK: USING MACHINE LEARNING TO IMPLEMENT SELECTIVE MFA IN MODERN BANKING PLATFORMS , 2019.
Pozzolo, A. D., Boracchi, G., Caelen, O., Alippi, C., & Bontempi, G. (2018). Credit card fraud detection: A realistic modeling and a novel learning strategy. IEEE Transactions on Neural Networks and Learning Systems, 29(8), 3784–3797.
Carcillo, F., Dal Pozzolo, A., Le Borgne, Y. A., Caelen, O., Mazzer, Y., & Bontempi, G. (2019). Scarff: A scalable framework for streaming credit card fraud detection with Spark. Information Fusion, 41, 182–194.
Roy, A., Sun, J., Mahoney, W., Alshboul, R., & Prabakar, N. (2018). Deep learning detecting fraud in credit card transactions. In Proceedings of the IEEE International Conference on Big Data (pp. 1846–1855).