AI-Driven Code Generation and Debugging

Abstract

In software engineering, artificial intelligence
(AI) has become a revolutionary force,
especially in the areas of intelligent debugging
and automated code production.
Large
language models (LLMs), neural program
synthesis, and sophisticated deep learning
architectures enable modern AI systems to
carry out tasks that formerly required a high
level of human competence. These systems
produce
syntactically
valid,
logically
consistent, and context-aware code snippets by
analyzing large repositories of source code,
programming patterns, and plain language
instructions.Additionally, AI-driven debugging
tools use probabilistic reasoning, static and
dynamic code analysis, and machine learning
techniques to find, locate, and fix software
flaws. AI-driven development has therefore
greatly accelerated software production cycles
while improving code maintainability and
dependability.
This research paper provides a comprehensive
study of AI-driven code generation and
debugging, examining the architectures,
training methodologies, application areas, and
performance
evaluation
metrics
of
state-of-the-art models. The abstract also
highlights how transformer-based models such
as OpenAI Codex, GPT-series models,
AlphaCode, and Meta’s Code Llama have
revolutionized automated programming by
enabling natural language to code translation,
automated refactoring, and intelligent code
completion. Additionally, the paper discusses
AI-powered
debugging
systems
like
Facebook’s SapFix and automated patch generation tools that streamline error detection
and correction.