Software Defect

Software defects, costing billions annually, represent a significant challenge in software development, with research focusing on efficient detection and automated repair. Current efforts leverage machine learning, particularly deep learning models like transformers and large language models (LLMs), to improve bug localization, predict defect locations (e.g., at the line level), and even automatically generate fixes or suggest verification questions to refine LLM-generated code. These advancements aim to reduce the substantial developer time spent on debugging and improve software quality and reliability, impacting both software engineering practices and the broader field of AI-assisted software development.