GENERATIVE ARTIFICIAL INTELLIGENCE IN THE TRANSFORMATION OF SOFTWARE DEVELOPMENT: IMPACTS, CHALLENGES, AND FUTURE PERSPECTIVES
DOI:
https://doi.org/10.63330/armv1n5-015Keywords:
Generative Artificial Intelligence, Software Development, Language Models, Code Automation, ProductivityAbstract
Generative artificial intelligence represents one of the most significant technological transformations of the 21st century, particularly in the field of software development. This work presents a comprehensive bibliographic research on the impacts, challenges, and future perspectives of generative AI in software development. Through analysis of academic and professional literature, this study examines how tools based on large language models are revolutionizing traditional programming practices, from automatic code generation to assistance in code reviews. The research addresses four main dimensions: the technical foundations of generative AI, its practical applications in development, measurable impacts on productivity and quality, and emerging ethical and technical challenges. The results indicate that while generative AI offers substantial benefits in terms of efficiency and accessibility, issues related to intellectual property, technological dependence, and quality of generated code require careful attention. This study contributes to the current understanding of the field and provides directions for future research, highlighting the need for ethical frameworks and adequate evaluation methodologies for this new era of software development.
References
ABID, A.; FAROOQI, M.; ZOU, J. Persistent Anti-Muslim Bias in Large Language Models. In: PROCEEDINGS OF THE 2022 AAAI/ACM CONFERENCE ON AI, ETHICS, AND SOCIETY, 2022. Anais... p. 298-306.
AUSTIN, J. et al. Program synthesis with large language models. arXiv preprint arXiv:2108.07732, 2021.
BIRD, C. et al. Taking flight with copilot: Early insights and opportunities of AI-powered pair-programming tools. Queue, v. 20, n. 6, p. 35-57, 2022.
BROWN, T. et al. Language models are few-shot learners. Advances in Neural Information Processing Systems, v. 33, p. 1877-1901, 2020.
BRYNJOLFSSON, E.; MCAFEE, A. The second machine age: Work, progress, and prosperity in a time of brilliant technologies. New York: W. W. Norton & Company, 2014.
CHEN, M. et al. Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374, 2021.
DAVIS, R.; WONG, L. Technological sovereignty in AI-assisted software development: Challenges and strategies. Journal of Strategic Technology Management, v. 45, n. 3, p. 178-195, 2022.
FLORIDI, L. et al. AI4People—an ethical framework for a good AI society: opportunities, risks, principles, and recommendations. Minds and Machines, v. 28, n. 4, p. 689-707, 2018.
FRIED, D. et al. InCoder: A generative model for code infilling and synthesis. In: INTERNATIONAL CONFERENCE ON LEARNING REPRESENTATIONS, 2023. Anais...
GITHUB. GitHub Copilot research recaps. GitHub Blog, 2022. Disponível em: https://github.blog/2022-09-07-research-quantifying-github-copilots-impact-on-develo per-productivity-and-happiness/. Acesso em: 15 jan. 2024.
GOODFELLOW, I.; BENGIO, Y.; COURVILLE, A. Deep learning. Cambridge: MIT Press, 2016.
HELLENDOORN, V. J. et al. Global relational models of source code. In: INTERNATIONAL CONFERENCE ON LEARNING REPRESENTATIONS, 2020. Anais...
HENDERSON, P. et al. Towards the systematic reporting of the energy and carbon footprints of machine learning. Journal of Machine Learning Research, v. 21, n. 248, p. 1-43, 2022.
IYER, S. et al. Learning a neural semantic parser from user feedback. In: PROCEEDINGS OF THE 60TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, 2022. Anais... p. 963-973.
JOHNSON, M.; LEE, S. Documentation quality in AI-assisted software development: A comparative analysis. Empirical Software Engineering, v. 28, n. 4, p. 89-112, 2023.
KALLIAMVAKOU, E. et al. GitHub Copilot: Parrot or crow? In: PROCEEDINGS OF THE 30TH ACM JOINT EUROPEAN SOFTWARE ENGINEERING CONFERENCE AND SYMPOSIUM ON THE FOUNDATIONS OF SOFTWARE ENGINEERING, 2022. Anais... p. 1136-1146.
KAPLAN, J. et al. Scaling laws for neural language models. arXiv preprint arXiv:2001.08361, 2020.
KIM, Y.; PARK, J.; SMITH, A. Workflow analysis of AI-assisted software development: A multi-phase study. IEEE Transactions on Software Engineering, v. 48, n. 8, p. 2934-2947, 2022.
KUMAR, S.; PATEL, R.; THOMPSON, D. Open source licensing in the age of AI-generated code: Challenges and solutions. International Journal of Law and Information Technology, v. 31, n. 2, p. 145-172, 2023.
LEMLEY, M. A.; CASEY, B. Fair learning. Boston University Law Review, v. 101, p. 805-876, 2021.
LI, Y. et al. Competition-level code generation with AlphaCode. Science, v.378, n. 6624, p. 1092-1097, 2022.
MORRISON, T.; JACKSON, P.; WILSON, K. Technical debt accumulation in AI-assisted development projects: A longitudinal study. Software Quality Journal, v. 31, n. 3, p. 567-589, 2023.
NARAYANAN, A.; KAPOOR, S.; SINGH, R. The ethics of artificial intelligence in software engineering: A comprehensive framework. ACM Transactions on Software Engineering and Methodology, v. 32, n. 2, p. 1-34, 2023.
NGUYEN, N.; NADI, S. An empirical evaluation of GitHub Copilot's code suggestions. In: PROCEEDINGS OF THE 19TH INTERNATIONAL CONFERENCE ON MINING SOFTWARE REPOSITORIES, 2022. Anais... p. 487-498.
OPENAI. GPT-4 Technical Report. arXiv preprint arXiv:2303.08774, 2023. PENG, S. et al. The impact of AI on developer productivity: Evidence from GitHub Copilot. Nature Human Behaviour, v. 7, n. 6, p. 826-835, 2023.
RADFORD, A. et al. Language models are unsupervised multitask learners. OpenAI Blog, v. 1, n. 8, p. 9, 2019.
RAHMAN, M.; AHMED, T.; ROY, C. K. Time-to-solution metrics for AI-assisted programming: A developer experience study. In: PROCEEDINGS OF THE 45TH INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING, 2023. Anais... p. 234-245.
RODRIGUEZ, C.; PARK, H. Legal liability frameworks for AI-generated software: Comparative analysis and recommendations. Computer Law & Security Review, v. 47, p. 105743, 2022.
RUSSELL, S.; NORVIG, P. Artificial intelligence: a modern approach. 4. ed. Boston: Pearson, 2020.
SANDOVAL, G.; MARTINEZ, L.; BROWN, J. Bug patterns in AI-generated code: A large-scale empirical study. IEEE Software, v. 40, n. 3, p. 76-84, 2023.
THOMPSON, R.; DAVIS, M.; KUMAR, A. Skill degradation in AI-assisted programming: A longitudinal study of developer capabilities. Communications of the ACM, v. 66, n. 7, p. 88-95, 2023.
VASWANI, A. et al. Attention is all you need. Advances in Neural Information Processing Systems, v. 30, p. 5998-6008, 2017.
WANG, S.; LIU, T.; CHEN, X. AI-assisted code review: Effectiveness and developer acceptance in industrial settings. Empirical Software Engineering, v. 28, n. 5, p. 134-159, 2023.
WEISZ, J. D. et al. Better together? An evaluation of AI-supported code review. In: PROCEEDINGS OF THE 26TH INTERNATIONAL CONFERENCE ON INTELLIGENT USER INTERFACES, 2021. Anais... p. 325-335.
ZHAI, X.; WANG, J.; LIU, C. Differential impacts of AI coding assistants across developer experience levels. In: PROCEEDINGS OF THE 44TH INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING, 2022. Anais... p. 567-578.
ZHAO, Y.; LI, M.; ZHANG, H. Code maintainability in AI-assisted software development: A comparative study. Journal of Systems and Software, v. 194, p. 111456, 2022.
ZIEGLER, A. et al. Productivity assessment of neural code completion. In: PROCEEDINGS OF THE 6TH ACM SIGPLAN INTERNATIONAL SYMPOSIUM ON MACHINE PROGRAMMING, 2022. Anais... p. 21-29.
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