Programming has always been partially automated through compilers, templates, libraries, static analysis, and autocomplete, but the post-2020 arrival of code-capable large language models changed the scale of automation by making natural-language specification, example-based prompting, and conversational repair central to everyday development. Chen et al. (2021) established early evidence that large language models trained on code could solve a substantial subset of programming tasks, and Peng et al. (2023) reported in a controlled GitHub Copilot experiment that developers completed a JavaScript task 55.8% faster with AI assistance. These findings help explain the rapid adoption of copilots across industry, yet they should not be interpreted as evidence that programming has become a solved generation problem. Traditional tools remain strongest at enforcing syntax, type discipline, and reproducible builds, while AI copilots excel primarily at scaffolding, boilerplate generation, API recall, and translating intent into first drafts. The more open-ended the task becomes, the more visible the limits of current models: architecture decisions, requirement negotiation, debugging under uncertainty, and long-term maintainability still demand skills that are only weakly represented in next-token prediction. A critical research gap concerns the long-term effects of AI assistance on developer cognition and team capability formation, because short-horizon productivity gains may coexist with weaker code comprehension, overreliance on generated patterns, or reduced onboarding opportunities for novices. Future research should therefore examine programming not only as a code emission task but as a sociotechnical process involving review, testing, documentation, and learning. The most consequential direction is to build programming assistants that expose uncertainty, ask clarifying questions, and integrate with evidence from repositories and execution traces, thereby shifting the field from raw generation toward accountable collaboration between human developers and AI systems (Chen et al., 2021; Peng et al., 2023; Jimenez et al., 2023).
References1. Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H. P. D. O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., Ray, A., Puri, R., Krueger, G., Petrov, M., Khlaaf, H., Sastry, G., Mishkin, P., Chan, B., Gray, S., Ryder, N., Pavlov, M., Power, A., Kaiser, L., Bavarian, M., Winter, C., Tillet, P., Such, F. P., Cummings, D. W., Plappert, M., Chantzis, F., Barnes, E., Herbert-Voss, A., Guss, W. H., Nichol, A., Paino, A., Tezak, N., Tang, J., Babuschkin, I., Balaji, S., Jain, S., Saunders, W., Hesse, C., Carr, A. N., Leike, J., Achiam, J., Misra, V., Morikawa, E., Radford, A., Knight, M., Brundage, M., Murati, M., Mayer, K., Welinder, P., McGrew, B., Amodei, D., McCandlish, S., Sutskever, I., & Zaremba, W. (2021). Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374.
2. Peng, S., Kalliamvakou, E., Cihon, P., & Demirer, M. (2023). The impact of AI on developer productivity: Evidence from GitHub Copilot. arXiv preprint arXiv:2302.06590.
3. Jimenez, C. E., Yang, J., Wettig, A., Yao, S., Pei, K., Press, O., & Narasimhan, K. (2023). SWE-bench: Can language models resolve real-world GitHub issues? arXiv preprint arXiv:2310.06770.
S. M. Monowar KayserLecturer, Department of Multimedia & Creative Technology (MCT)
Faculty of Science & Information Technology
Daffodil International University (DIU)
Daffodil Smart City, Savar, Dhaka, Bangladesh
Visit: https://monowarkayser.com/
