AI-Assisted Programming, Memory Safety, Multi-Paradigm Design and Emerging Development Models

Year : 2026 | Volume : 13 | Issue : 02 | Page : 43 50
By

Nidhi Chahal,

Purshotam Jha,

Sonia,

Vaishanvi,

  1. Assistant Professor, Electronics and Communication Engineering, Chandigarh Engineering College, Punjab, India
  2. Student, Electronics and Communication Engineering, Chandigarh Engineering College, Punjab, India
  3. Student, Electronics and Communication Engineering, Chandigarh Engineering College, Punjab, India
  4. Student, Electronics and Communication Engineering, Chandigarh Engineering College, Punjab, India

Abstract

As the field of artificial intelligence (AI) continues to evolve at a fast pace, and specifically with the rise of large language models (LLMs), the software design, implementation, testing, and maintenance landscape has undergone unprecedented transformation. Although programming languages are still the main language for writing computational logic, the way developers interact with programming languages is now increasingly mediated by AI-based tools. Current AI coding systems can help with code generation, program explanation, error detection, test case generation, program translation between languages, and software documentation. This process is introducing a new type of programming that relies on AI to generate code, but with the human developer setting out requirements and ensuring the machine code meets them rather than writing all the lines. In this paper, we provide a selective overview of the recent advances in the field of programming languages and AI. It covers AI code generation, LLMs for software engineering, AI agents, natural language programming, memory-safe programming, and the evolving status of modern programming languages like Python, Rust, and TypeScript. There is significant evidence that shows that AI development tools are in use, but the problems of correctness, security, privacy, and verification are still substantial. The paper thus examines opportunities and challenges of AI-aided programming and suggests a human-in-the-loop approach to development that combines the process of generation, static analysis, testing, security validation, and human review. Finally, new research directions are explored, such as AI-native programming environments, verified code generation, agentic software development, and programming languages for reliable human–AI collaboration.

Keywords: Artificial intelligence, programming languages, large language models, AI-assisted programming, code generation, AI agents, software engineering, memory safety, human–AI collaboration

[This article belongs to Recent Trends in Programming languages ]

How to cite this article: Nidhi Chahal, Purshotam Jha, Sonia, Vaishanvi. AI-Assisted Programming, Memory Safety, Multi-Paradigm Design and Emerging Development Models. Recent Trends in Programming languages. 2026; 13(02):43-50.
How to cite this URL: Nidhi Chahal, Purshotam Jha, Sonia, Vaishanvi. AI-Assisted Programming, Memory Safety, Multi-Paradigm Design and Emerging Development Models. Recent Trends in Programming languages. 2026; 13(02):43-50. Available from: https://journals.stmjournals.com/rtpl/article=2026/view=259986

References

  1. Stack Overflow. (2025). AI: 2025 Stack Overflow Developer Survey. [online] Stack Overflow. Available from: https://survey.stackoverflow.co/2025/ai.
  2. Hou X, Zhao Y, Liu Y, Yang Z, Wang K, Li L, et al. Large language models for software engineering: a systematic literature review. ACM Trans Softw Eng Methodol. 2024;33(8):1-79. doi:10.1145/3695988.
  3. Jiang J, Wang F, Shen J, Kim S, Kim S. A survey on large language models for code generation. ACM Trans Softw Eng Methodol. 2026;35(2):1-72. doi:10.1145/3747588.
  4. He J, Treude C, Lo D. LLM-based multi-agent systems for software engineering: literature review, vision, and the road ahead. ACM Trans Softw Eng Methodol. 2025;34(5):1-30.
  5. Rust Project Developers. (2025). The Rust programming language [Online]. Rust Programming Language Documentation. Available from: https://doc.rust-lang.org/book/.
  6. (2025). TypeScript handbook [Online]. TypeScript Documentation. Available from: https://www.typescriptlang.org/docs/handbook/.
  7. Fan DP, Ji GP, Sun G, Cheng MM, Shen J, Shao L. Camouflaged object detection. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 2020. p. 2774-2784. doi:10.1109/CVPR42600.2020.00285.
  8. Sun Y, Chen G, Zhou T, Zhang Y, Liu N. Context-aware cross-level fusion network for camouflaged object detection. In: Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization; 2021. p. 1025-1031. doi:10.24963/ijcai.2021/142.
  9. Chen G, Liu SJ, Sun YJ, Ji GP, Wu YF, Zhou T. Camouflaged object detection via context-aware cross-level fusion. IEEE Trans Circuits Syst Video Technol. 2022;32(10):6981-6993. doi:10.1109/TCSVT.2022.3178173.
  10. Zhang S, Kong D, Xing Y, Lu Y, Ran L, Liang G, et al. Frequency-guided spatial adaptation for camouflaged object detection. IEEE Trans Multimed. 2025;27:72-83. doi:10.1109/TMM.2024.3521681
Regular Issue Subscription Original Research
Volume 13
Issue 02
Received 26/09/2026
Accepted 28/09/2026
Published 29/09/2026
Publication Time 3 Days

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