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@article{176772,
        author = {Abhishek Kumar and Aryan Tyagi and Ayush jha and Himanshu},
        title = {Neural Networks in Code Generation How AI is Changing Software Development},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {6627-6633},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=176772},
        abstract = {Artificial Intelligence (AI), increasingly through the use of neural networks, is transforming the software development practice by facilitating automated code generation and smart development support. With the advent of transformer-based models like GPT, Codex, and CodeT5, AI systems can now read natural language, generate syntactically and semantically valid code, aid in debugging, generate documentation, translate code from one programming language to another, and generate unit tests. They are trained on enormous codebases and technical material, allowing them to learn intricate programming patterns and code.
This essay examines the architecture and operation of neural networks in programming code generation, specifically the groundbreaking advancements of transformer models. It presents an in-depth description of the evolution of AI in software from rule-based systems to the current deep learning systems. Using basic charts and mundane examples, it enlightens the reader about the integration of AI tools such as GitHub Copilot, Amazon CodeWhisperer, and TabNine into contemporary development processes.
Although the advantages—greater productivity, better quality code, and reduced onboarding time—are considerable, the paper also discusses the drawbacks of code correctness, IP issues, security vulnerabilities, and ethics. The case studies record both the practical advantages and possible drawbacks of embracing neural code generation. The paper concludes with hints towards future possibilities such as personal AI assistants, context awareness, and integration with CI/CD pipelines, ultimately identifying human intervention in AI-enabled software development.},
        keywords = {Neural Networks, Code Generation, Artificial Intelligence, Transformers, GPT, Codex, GitHub Copilot, Software Development, Deep Learning, Programming Automation, Machine Learning, AI Tools, Code Completion, AI in IDEs, DevOps, Secure Coding.},
        month = {April},
        }