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Integrating Artificial Intelligence in Software Engineering: Enhancements and Challenges in the Development Lifecycle
Subject area: Science,Engineering and Technology · Area of research: Artificial Intelligence
Abstract
The integration of artificial intelligence (AI) in software engineering is revolutionizing the traditional software development lifecycle. This research paper explores the multifaceted role of AI in enhancing software engineering practices, focusing on coding, testing, and maintenance. By automating repetitive tasks, AI improves efficiency and quality in software development. Intelligent code assistants, automated test case generation, and AI-driven bug fixing are just a few examples of how AI is transforming the industry. However, the incorporation of AI also introduces challenges, such as the need for high-quality training data, explainable AI models, and seamless integration with existing processes. This study reviews current literature, highlights key findings, and identifies gaps where further research is needed. Through a comprehensive analysis, this paper aims to provide a deeper understanding of the potential and challenges of AI in software engineering, offering insights into future research directions and the evolution of AI-enhanced development practices.
References
[1] Monperrus, M. (2019). Automatic software repair: A bibliography. ACM Computing Surveys, 51(1), 1-24.
[2] Brockschmidt, M., Allamanis, M., Gaunt, A. L., & Polozov, O. (2018). Generative Code Modeling with Graphs. arXiv:1805.08490.
[3] Anand, S., Burke, E. K., Chen, T. Y., Clark, J., Cohen, M. B., Grieskamp, W., ... & McMinn, P. (2013). An orchestrated survey of methodologies for automated software test case generation. Journal of Systems and Software, 86(8), 1978-2001.
[4] Saeid, H. (2020). Revolutionizing Software Engineering: Leveraging AI for Enhanced Development Lifecycle. International Journal of Innovative Research in Engineering & Multidisciplinary Physical Sciences, 8(1).
[5] Weyns, D., Iftikhar, M. U., De La Iglesia, D. G., & Ahmad, T. (2012). A survey of formal methods in self-adaptive systems.
[6] He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770-778).
How to cite this paper
@article{1702368,
author = {Nadia Chafik, Dr Amine Benchekroun},
title = {Integrating Artificial Intelligence in Software Engineering: Enhancements and Challenges in the Development Lifecycle},
journal = {Iconic Research And Engineering Journals},
year = {2020},
volume = {3},
number = {12},
pages = {253-265},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1702368.pdf},
abstract = {The integration of artificial intelligence (AI) in software engineering is revolutionizing the traditional software development lifecycle. This research paper explores the multifaceted role of AI in enhancing software engineering practices, focusing on coding, testing, and maintenance. By automating repetitive tasks, AI improves efficiency and quality in software development. Intelligent code assistants, automated test case generation, and AI-driven bug fixing are just a few examples of how AI is transforming the industry. However, the incorporation of AI also introduces challenges, such as the need for high-quality training data, explainable AI models, and seamless integration with existing processes. This study reviews current literature, highlights key findings, and identifies gaps where further research is needed. Through a comprehensive analysis, this paper aims to provide a deeper understanding of the potential and challenges of AI in software engineering, offering insights into future research directions and the evolution of AI-enhanced development practices.},
month = {June},
}