Smart Debugging: AI Approaches to Tackling Java Memory Leaks

EasyChair Preprint 14725

Abstract

Java memory leaks present a significant challenge for developers, often leading to degraded performance and system instability. "Smart Debugging: AI Approaches to Tackling Java Memory Leaks" explores innovative artificial intelligence techniques designedtoaddressandmitigatetheseissues.Thisarticleexaminestheintegration of AI-driven tools and methodologies, including machine learning algorithms and anomaly detection, to identify, analyze, and resolve memory leaks in Java applications more efficiently. By leveraging predictive models and automated analysis, these AI approaches enhance the debugging process, offering precise insights into memory usage patterns and leak origins. The paper presents a comparative evaluation of traditional debugging methods versus AI-enhanced strategies, highlighting improvements in detection accuracy, resolution speed, and overall system stability. The findings underscore the potential of AI to transform memory leak management, providing a forward-looking perspective on the future of softwaredebugging.

Keyphrases: AI, Debugging, leak, management, memory, perspective, software

@booklet{EasyChair:14725,
  author    = {Edwin Frank},
  title     = {Smart Debugging: AI Approaches to Tackling Java Memory Leaks},
  howpublished = {EasyChair Preprint 14725},
  year      = {EasyChair, 2024}}