A Comprehensive Approach to Software Development Lifecycle Models: From Waterfall to Agile
Abstract
The software development lifecycle (SDLC) models have evolved significantly over the years, from the traditional Waterfall approach to Agile methodologies. This paper provides a comprehensive overview of various SDLC models, including Waterfall, V-Model, Spiral, and Agile, comparing their advantages and limitations. Through an analysis of real-world case studies, the paper highlights how organizations have adopted these models based on project requirements, risk factors, and team size. The research also emphasizes the growing importance of Agile methods in fostering collaboration, flexibility, and faster delivery. It concludes with a discussion on the future of SDLC models and the potential integration of hybrid approaches to accommodate diverse project needs in an ever-changing software development landscape.
References
Gupta, M., Jain, J., Agarwal, G., Modake, R., & Tanikonda, A. (2025). Adversarial Attacks and Fraud Defenses: Leveraging Data Engineering to Secure AI Models in the Digital Age. Authorea Preprints, 20.
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
Bahnsen, A. C., Aouada, D., Stojanovic, A., & Ottersten, B. (2016). Feature engineering strategies for credit card fraud detection. In Proceedings of the 2016 International Conference on Machine Learning and Applications (pp. 1129–1135).
Fiore, U., De Santis, A., Perla, F., Zanetti, P., & Palmieri, F. (2019). Using generative adversarial networks for improving classification effectiveness in credit card fraud detection. Information Sciences, 479, 448–455.
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.
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).
Kim, Y., & Kim, D. (2021). Financial fraud detection using machine learning and deep learning algorithms. Journal of Intelligent & Fuzzy Systems, 40(5), 9175–9185.
