"AI-Driven Code Completion and Optimization: Enhancing Developer Efficiency"

EasyChair Preprint 13856

Abstract

In the rapidly evolving landscape of software development, AI-driven tools have emerged as critical assets for enhancing developer efficiency. This research explores the impact of AI-driven code completion and optimization techniques on software development processes. Leveraging advanced machine learning algorithms and natural language processing, AI-driven tools can predict and suggest code snippets, detect potential errors, and optimize code for performance. This study evaluates the effectiveness of these tools through a series of experiments, demonstrating significant improvements in coding speed, accuracy, and overall developer productivity. The findings highlight the transformative potential of AI in the realm of software engineering, paving the way for more efficient and error-free development cycles.

Keyphrases: AI-driven code completion, Code Prediction, Developer efficiency, Natural Language Processing, Productivity enhancement, Software Engineering, code optimization, error detection, machine learning, software development

@booklet{EasyChair:13856,
  author    = {Kayode Sheriffdeen},
  title     = {"AI-Driven Code Completion and Optimization: Enhancing Developer Efficiency"},
  howpublished = {EasyChair Preprint 13856},
  year      = {EasyChair, 2024}}