International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 05 | May 2025
p-ISSN: 2395-0072
www.irjet.net
A REVIEW OF ARTIFICIAL INTELLIGENCE IN PROGRAMMING: TRANSFORMING LANGUAGE STRUCTURES AND CAPABILITIES Kamil Ansari1, Dr. Peeyush Kumar Pathak2 1Master of Technology, Computer Science and Engineering, Goel Institute of Technology And Management,
Lucknow, India
2Assistant Professor, Department of Computer Science and Engineering, Goel Institute of Technology And
Management, Lucknow, India ---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - Now it is turning the writing of programming
with code and semantic analysis as to how artificial intelligences will distinguish their tasks that this will pose such a radical shift for the future programming world, and so we have to reconsider how languages and tools will be designed, deployed and interacted with. For the first time, programming languages have become mainstream enough to cover human logic and to offer the way for this logic to be executed in machines; AI introduces the first recognizable needs and opportunities to match language structures with machine learning powered automation, abstraction and reasoning.
into a revolution and also transforming how languages are written, how code is generated and how developers communicate with computational systems. As a review paper, this paper discusses the use of AI for transforming programming language structures: syntax simplification and semantic enrichment, and adding developer capabilities with automated code generation (GitHub Copilot, AlphaCode) and AI based debugging. Because of this, AI facilitates natural language intent and machine execution, and facilitates intuitive programming paradigms across all of software development, bringing up very important questions: What biases exist in AI trained data, of which AI is so dependent on; as well as security vulnerabilities in AI using legal bind in terms of its dependency on code. Finally, low code cases and AI in competitive programming are presented as a trap and a promise of human AI collaboration. The paper claims that while proper and advanced AI is not an addition to traditional programmers, but instead it constructs the framework for programming languages by constructing adaptive systems that fulfill to maximality of scalability and performance. This however has to guarantee fair frameworks to address fairness, security, intellectual property problem and pedagogical reform in an agreement with automation and the growth of basic skill development. As AI evolves, their symbiosis in promoting humans’ creativity can yield an unprecedented innovation if some foresight is possible on the technical, ethical, and pedagogical side.
1.2 Purpose And Scope In this review we study how AI alters programming languages and developer workflow, specifically how AI augments the syntax and semantics of language, and allows programming beyond its syntax—automation, and at a higher level. To that end, it discusses AI innovations such as natural language to code translation, adaptive language semantics, and intelligent debugging systems and their possible effect on low and high level programming tasks and design philosophies. This paper investigates how AI redefines the productivity tool role of AI itself and propels it into an architectural force in which the evolution of what programming languages can express and what it takes to build those languages are reconfigured.
Key Words: Artificial Intelligence in programming, automated code generation, AI-driven language design, natural language processing (NLP), AI ethics, programming education, low-code platforms, AI-human collaboration.
1. INTRODUCTION 1.1 Background This illustrates that with the swift rise of AI, it has and will continue to have a significant role in altering the premise behind which software is being developed, how some of the traditional ways of solving impossible computational problems are rewritten. But it is near enough our work
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