AI-Driven Code Automation: Intelligent Algorithms for Suggesting and Generating Programming Code

Session

Computer Science and Communication Engineering

Description

Artificial Intelligence (AI) is increasingly transforming software development by enabling intelligent code automation, which can significantly enhance productivity and reduce human errors. This research investigates AI-driven algorithms designed to suggest or generate code snippets automatically, leveraging advanced machine learning techniques such as deep learning and natural language processing. The study analyzes how these algorithms understand programming context, recognize patterns in existing codebases, and produce syntactically and semantically correct code. Additionally, it explores the challenges of integrating AI-generated code into larger projects, maintaining code quality, and managing ambiguous or incomplete requirements. The research also examines current tools and frameworks, including transformer- based models and intelligent code assistants, assessing their effectiveness in real-world programming tasks. By highlighting the potential of AI-powered code automation, this study demonstrates how developers can focus more on higher-level problem-solving and software design, while routine coding tasks are efficiently handled by intelligent systems. The findings suggest that AI-driven code generation can play a pivotal role in shaping the future of software engineering and accelerating innovation.

Keywords:

AI-driven code generation, intelligent automation, machine learning, software development

Proceedings Editor

Edmond Hajrizi

ISBN

978-9951-982-41-2

Location

UBT Kampus, Lipjan

Start Date

25-10-2025 9:00 AM

End Date

26-10-2025 6:00 PM

DOI

10.33107/ubt-ic.2025.70

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Oct 25th, 9:00 AM Oct 26th, 6:00 PM

AI-Driven Code Automation: Intelligent Algorithms for Suggesting and Generating Programming Code

UBT Kampus, Lipjan

Artificial Intelligence (AI) is increasingly transforming software development by enabling intelligent code automation, which can significantly enhance productivity and reduce human errors. This research investigates AI-driven algorithms designed to suggest or generate code snippets automatically, leveraging advanced machine learning techniques such as deep learning and natural language processing. The study analyzes how these algorithms understand programming context, recognize patterns in existing codebases, and produce syntactically and semantically correct code. Additionally, it explores the challenges of integrating AI-generated code into larger projects, maintaining code quality, and managing ambiguous or incomplete requirements. The research also examines current tools and frameworks, including transformer- based models and intelligent code assistants, assessing their effectiveness in real-world programming tasks. By highlighting the potential of AI-powered code automation, this study demonstrates how developers can focus more on higher-level problem-solving and software design, while routine coding tasks are efficiently handled by intelligent systems. The findings suggest that AI-driven code generation can play a pivotal role in shaping the future of software engineering and accelerating innovation.