9798337344607 - Automating Software Defect Detection Through Machine Learning and Llms (5 results)
- Hardcover
Seller: PBShop.store US, Wood Dale, IL, U.S.A.PBShop.store US
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£ 217.50
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HRD. Condition: New. New Book. Shipped from UK. Established seller since 2000.
- Hardcover
Seller: PBShop.store UK, Fairford, GLOS, United KingdomPBShop.store UK
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£ 202.92
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HRD. Condition: New. New Book. Shipped from UK. Established seller since 2000.
- Hardcover
- Print on Demand
Seller: CitiRetail, Stevenage, United KingdomCitiRetail
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£ 212.49
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Hardcover. Condition: new. Hardcover. 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 our UK warehouse or from our Australian or US warehouses, depending on stock availability.
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- Hardcover
- Print on Demand
Seller: preigu, Osnabrück, Germanypreigu
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£ 264.29
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Buch. Condition: Neu. Automating Software Defect Detection Through Machine Learning and LLMs | Bryan Gardiner (u. a.) | Buch | Englisch | 2025 | IGI Global | EAN 9798337344607 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.
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- Hardcover
- Print on Demand
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
Contact seller5-star sellerCondition: New
£ 339.83
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Buch. Condition: Neu. 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 l…imitations, 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.
