Automating Software Defect Detection Through Machine Learning and LLMs

Language: English

Published by IGI Global, 2025

9798337344607

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  • New
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Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH

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nach der Bestellung gedruckt Neuware - Printed after ordering - 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.

Seller Inventory # 9798337344607

Title
Automating Software Defect Detection Through Machine Learning and LLMs
Author
Bryan Gardiner
Publisher
IGI Global
Publication year
2025
Condition
Neu
Binding
Buch
Language
English
ISBN 13
9798337344607
Item weight
995 grams
Dimensions
260x183x27 mm

AHA-BUCH GmbH

Einbeck, Germany

5-star seller

AbeBooks seller since August 14, 2006

Shipping rates from Germany to U.S.A.

Item5 to 7 business days7 to 10 business days
First itemUS$ 35.42US$ 35.42
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