Advancements in software engineering using AI
Vol 6, Issue 1, 2023
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Abstract
The integration of Artificial Intelligence (AI) into the space of software engineering marks a transformative period that reshapes traditional development processes and propels the industry into a new era of innovation. This exploration delves into the multifaceted impact of AI, from its roots in early symbolic AI to the contemporary dominance of machine learning and deep learning. AI’s applications span various domains, but its significance in software engineering lies in its ability to enhance efficiency, improve software quality, and introduce novel approaches to problem-solving. From automating routine tasks to streamlining complex development workflows, AI acts as a virtual collaborator, allowing human developers to focus on higher-order thinking and creativity. This study introduces the application of AI in software engineering. reverse-engineering, and development environments. Moreover, ethical considerations, challenges, and future trends, including explainable AI, reinforcement learning, and human-AI collaboration, are presented.
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1. Makridakis S. The forthcoming Artificial Intelligence (AI) revolution: Its impact on society and firms. Futures 2017; 90: 46–60. doi: 10.1016/j.futures.2017.03.006
2. Smith RG, Eckroth J. Robert S. Building AI applications: Yesterday, today, and tomorrow. AI Magazine 2017; 38(1): 6–22. doi: 10.1609/aimag.v38i1.2709
3. Jiang F, Jiang Y, Zhi H, et al. Artificial intelligence in healthcare: Past, present and future. Stroke and Vascular Neurology 2017; 2(4): 230–243. doi: 10.1136/svn-2017-000101
4. Cao L. AI in finance: Challenges, techniques, and opportunities. ACM Computing Surveys 2022; 55(3): 1–38. doi: 10.1145/3502289
5. Ma Y, Wang Z, Yang H, et al. Artificial intelligence applications in the development of autonomous vehicles: A survey. IEEE/CAA Journal of AutomaticaSinica2020; 7(2): 315–329. doi: 10.1109/jas.2020.1003021
6. Mattas PS. ChatGPT: A study of AI language processing and its implications. International Journal of Research Publication and Reviews 2023; 4(2): 435–440.doi: 10.55248/gengpi.2023.4218
7. Isaev EA, Kornilov VV, Grigoriev AA. Data center efficiency model: A new approach and the role of Artificial Intelligence. Mathematical Biology and Bioinformatics 2023; 18(1): 215–227. doi: 10.17537/2023.18.215
8. Salvaris M, Dean D, Tok WH. Microsoft AI platform. In: Deep Learning with Azure. Apress; 2018. pp. 79–98. doi: 10.1007/978-1-4842-3679-6_4
9. Yetistiren B, Ozsoy I, Tuzun E. Assessing the quality of GitHub copilot’s code generation. In: Proceedings of the 18th International Conference on Predictive Models and Data Analytics in Software Engineering; 17 November 2022; Singapore, Singapore. doi: 10.1145/3558489.3559072
10. Finnie-Ansley J, Denny P, Becker BA, et al. The robots are coming: Exploring the implications of OpenAI Codex on introductory programming. In: Proceedings of the 24th Australasian Computing Education Conference; 14–18 February 2022; Online conference. doi: 10.1145/3511861.3511863
11. Magistretti S, Dell’Era C, Messeni Petruzzelli A. How intelligent is Watson? Enabling digital transformation through artificial intelligence. Business Horizons 2019; 62(6): 819–829. doi: 10.1016/j.bushor.2019.08.004
12. Barredo Arrieta A, Díaz-Rodríguez N, Del Ser J, et al. Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information Fusion 2020; 58: 82–115. doi: 10.1016/j.inffus.2019.12.012
13. Morales EF, Escalante HJ. A brief introduction to supervised, unsupervised, and reinforcement learning. In: Biosignal Processing and Classification Using Computational Learning and Intelligence. Academic Press; 2022. pp. 111–129. doi: 10.1016/b978-0-12-820125-1.00017-8
14. Lwakatare LE, Crnkovic I, Bosch J. DevOps for AI—Challenges in development of AI-enabled applications. In: Proceedings of 2020 International Conference on Software, Telecommunications and Computer Networks (SoftCOM); 17–19 September 2020; Online conference. doi: 10.23919/softcom50211.2020.9238323
15. Sabharwal N, Agrawal A. Up and Running Google AutoML and AI Platform: Building Machine Learning and NLP Models Using AutoML and AI Platform for Production Environment. BPB Publications; 2021.
DOI: https://doi.org/10.24294/csma.v6i1.3906
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