Journal of Advanced Engineering Technology and Management
ISSN (Online): 3049-3684
Volume: 1 Issue: 1 | Open Access | 14 Feb 2025
AI’s Next Leap In Redefining Enterprise Software Development: From Past to Future Possibilities
Syed Younus, Student, Osmania University
Abstract
Software Development as a field is exponentially growing. It is deemed essential to innovate and lead. Though technologies from time to time have greatly impacted the development methodologies, it is AI now that has ushered software development into a new era of possibilities. It truly has helped with optimizing the SDLC (Software Development Lifecycles), but there are myriad challenges that its integration into software engineering entails. AI tools are fast transforming the ways the codes are written, generated, improved, optimized, reviewed, debugged, and managed. From saving time that often goes into carrying out repetitive tasks to analyzing or testing code, AI tools have made almost all things simple. NLP (Natural Language Processing) is one of the best core features of AI-human interaction. The users provide queries in simple English and get the results they need to speed up their development process. This paper highlights the current practices, strategies, methodologies, and procedures enterprises employ to speed up their development processes. It systematically reviews the existing literature and discusses the difficulties underlying AI integration into enterprise software development practices, the complex nature of the AI models that the companies use, data storage, and data security. Further, it analyzes the AI-human collaboration from different perspectives, and how it fosters a culture of continuous learning, enabling the developers to learn and grow their development skills too. It also sheds light on the use of the current practices and what the future possibilities are.
Keywords: Software Development, Software Engineering, AI in Software Development, Artificial Intelligence in Software Engineering.
References
Khan, F., Kumar, R. L., Kadry, S., & Nam, Y. (2021). The future of software engineering: Visions of 2025 and beyond. International Journal of Electrical and Computer Engineering, 11(4), 3443-3450.
Fitzpatrick, R. (1996). Software quality: definitions and strategic issues.
Mohagheghi, P., Dehlen, V., & Neple, T. (2009). Definitions and approaches to model quality in model-based software development–A review of literature. Information and software technology, 51(12), 1646-1669.
Schott, V., & Ovtcharova, J. (2024). Integrating Sustainability into Software Development: A Global Categorization. In Finance and Law in the Metaverse World (pp. 59-69). Springer, Cham.
Yang, H., Nong, Y., Wang, S., & Cai, H. (2024). Multi-Language Software Development: Issues, Challenges, and Solutions. IEEE Transactions on Software Engineering.
Yazymov, M., Yarashova, G., & Atayev, A. (2024). MODERN INNOVATIVE TECHNOLOGIES IN THE FIELD OF SOFTWARE. Всемирный ученый, 1(29), 286-293.
Becker, K., & Gottschlich, J. (2021, July). AI Programmer: autonomously creating software programs using genetic algorithms. In Proceedings of the Genetic and Evolutionary Computation Conference Companion (pp. 1513-1521).
Barenkamp, M., Rebstadt, J., & Thomas, O. (2020). Applications of AI in classical software engineering. AI Perspectives, 2(1), 1.
Alenezi, M., & Akour, M. (2025). AI-Driven Innovations in Software Engineering: A Review of Current Practices and Future Directions. Applied Sciences, 15(3), 1344.
Donohoe, P. (Ed.). (2012). Software product lines: Experience and research directions.
Askarov, E. (2022). ARTIFICIAL INTELLIGENCE AND SOFTWARE BASED ARTIFICIAL INTELLIGENCE. Oriental renaissance: Innovative, educational, natural and social sciences, 2(12), 722-728.
Padmanaban, P. H., & Sharma, Y. K. (2019). Implication of Artificial Intelligence in Software Development Life Cycle: A state of the art review. 2019 IJRRA all rights reserved.
Sunil Medepalli. (2025). AI In Modern Software Development: Current Practices, Challenges, and Future Possibilities. In AI In Modern Software Development: Current Practices, Challenges and Future Possibilities. (Vol. 10, Number 01, pp. 1–7). Zenodo. https://doi.org/10.5281/zenodo.14716031
Hourani, H., Hammad, A., & Lafi, M. (2019, April). The impact of artificial intelligence on software testing. In 2019 IEEE Jordan International Joint Conference on Electrical Engineering and Information Technology (JEEIT) (pp. 565-570). IEEE.
Shin, J., & Nam, J. (2021). A survey of automatic code generation from natural language. Journal of Information Processing Systems, 17(3), 537-555.
Dehaerne, E., Dey, B., Halder, S., De Gendt, S., & Meert, W. (2022). Code generation using machine learning: A systematic review. Ieee Access, 10, 82434-82455.
Kamble, N., Khode, P., Dhabekar, V., Gode, S., Kumbhare, S., Wagh, Y., & Mohod, D. S. (2024). Code Generation Using AI and NLP Based Technique. Available at SSRN 4824877.
Pandy, G., Pugazhenthi, V. J., & Murugan, A. (2024). Advances in software testing in 2024: Experimental insights, frameworks, and future directions. International Journal of Advanced Research in Computer and Communication Engineering, 13(11), 40-44.
Khan, M. F. I., Mahmud, F. U., Hoseen, A., & Masum, A. K. M. (2024). A new approach of software test automation using ai. Journal of Basic Science and Engineering, 21(1), 559-570.
Odejide, O. A., & Edunjobi, T. E. (2024). AI in project management: exploring theoretical models for decision-making and risk management. Engineering Science & Technology Journal, 5(3), 1072-1085.
Shoushtari, F., Daghighi, A., & Ghafourian, E. (2024). Application of Artificial Intelligence in Project Management. International journal of industrial engineering and operational research, 6(2), 49-63.
Kiani, A. (2024). Artificial intelligence in entrepreneurial project management: a review, framework and research agenda. International Journal of Managing Projects in Business.
Coussement, K., Abedin, M. Z., Kraus, M., Maldonado, S., & Topuz, K. (2024). Explainable AI for enhanced decision-making. Decision Support Systems, 114276.
Anand, S., & Miglani, S. (2024). Real-time AI-driven predictive analytics for agile software development: Enhancing decision-making, resource optimization, and risk mitigation.
Biesialska, K., Franch, X., & Muntés-Mulero, V. (2021). Big Data analytics in Agile software development: A systematic mapping study. Information and Software Technology, 132, 106448.
Rahamathunnisa, U., Subhashini, P., Aancy, H. M., Meenakshi, S., & Boopathi, S. (2023). Solutions for Software Requirement Risks Using Artificial Intelligence Techniques. In Handbook of Research on Data Science and Cybersecurity Innovations in Industry 4.0 Technologies (pp. 45-64). IGI Global.
Sunil Medepalli. (2025). Human-AI Collaboration (HAIC): The Rise of Hybrid Intelligence in Modern Software Development. In Human-AI Collaboration (HAIC): The Rise of Hybrid Intelligence in Modern Software Development (1.00, Vol. 10, Number 1, pp. 1–6). Zenodo. https://doi.org/10.5281/zenodo.14743406
Ebert, C., & Louridas, P. (2023). Generative AI for software practitioners. IEEE Software, 40(4), 30-38.
Palomo-Duarte, M., García-Domínguez, A., & Balderas, A. (2021). Assessment in software development for competitive environments: An AI strategy development case study. Electronics, 10(13), 1566.
Deshmukh, A., Patil, D. S., Mohan, J. S., Balamurugan, G., & Tyagi, A. K. (2023). Transforming Next Generation-Based Artificial Intelligence for Software Development: Current Status, Issues, Challenges, and Future Opportunities. In Emerging Technologies and Digital Transformation in the Manufacturing Industry (pp. 30-66). IGI Global.
Matt, D. (2024). The implementation of AI technology in international companies: a leadership perspective (Doctoral dissertation, FH Vorarlberg (Fachhochschule Vorarlberg).