Automating Software Defect Detection Through Machine Learning and LLMs (Paperback)

Bryan Gardiner

ISBN 13: 9798337344614

Published by IGI Global, Hershey, PA, 2025

About this Item

Paperback. As software systems grow in complexity and scale, ensuring their reliability and quality becomes challenging. Traditional methods of defect detection are time-consuming, prone to errors, and inadequate for identifying issues. To address these limitations, the integration of machine learning (ML) techniques and large language models (LLMs) emerges as a transformative approach in automating software defect detection. ML algorithms can learn from historical bug data to predict vulnerabilities, while LLMs can detect anomalies with high accuracy. This convergence holds the potential to improve automation, software engineering, and defect detection, while introducing new challenges in interpretability, data bias, and model reliability that require further exploration. Automating Software Defect Detection Through Machine Learning and LLMs explores how cutting-edge technologies like machine learning (ML) and large language models (LLMs) transform software detection. It examines how these technologies enhance accuracy, scalability, and efficiency in identifying and mitigating software defects. This book covers topics such as algorithms, fraud detection, and software engineering, and is a useful resource for engineers, security professionals, academicians, researchers, and computer scientists. "This book provides a comprehensive understanding of how machine learning and large language models revolutionize the process of software defect detection"-- Provided by publisher. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Seller Inventory # 9798337344614

Bibliographic Details

• Title: Automating Software Defect Detection Through Machine Learning and LLMs (Paperback)
• Publisher: IGI Global, Hershey, PA
• Publication Date: 2025
• Language: English
• ISBN 13: 9798337344614
• Binding: Paperback
• Condition: new

About this title

Synopsis

As software systems grow in complexity and scale, ensuring their reliability and quality becomes challenging. Traditional methods of defect detection are time-consuming, prone to errors, and inadequate for identifying issues. To address these limitations, the integration of machine learning (ML) techniques and large language models (LLMs) emerges as a transformative approach in automating software defect detection. ML algorithms can learn from historical bug data to predict vulnerabilities, while LLMs can detect anomalies with high accuracy. This convergence holds the potential to improve automation, software engineering, and defect detection, while introducing new challenges in interpretability, data bias, and model reliability that require further exploration. Automating Software Defect Detection Through Machine Learning and LLMs explores how cutting-edge technologies like machine learning (ML) and large language models (LLMs) transform software detection. It examines how these technologies enhance accuracy, scalability, and efficiency in identifying and mitigating software defects. This book covers topics such as algorithms, fraud detection, and software engineering, and is a useful resource for engineers, security professionals, academicians, researchers, and computer scientists.