Automating Software Defect Detection Through Machine Learning and LLMs

Name: Automating Software Defect Detection Through Machine Learning and LLMs
Brand: IGI Global
SKU: 9798337344607
Price: 239.36 EUR
Availability: InStock

Autor: Pancham Singh, Prashant Upadhyay

Jazyk:

Väzba: Pevná

Vydavateľ: IGI Global

Dostupnosť: Skladom u dodávateľa

Odosielame za 10-18 dní

239.36 €

As software systems grow in complexity and scale, ensuring their reliability and quality becomes cha...

Informácie o knihe

Autor

Pancham Singh, Prashant Upadhyay

Jazyk

Angličtina

Väzba

Kniha - Pevná

Vydalo

2025

Stránok

428

EAN

9798337344607

Enbook ID

49880467

Vydavateľ

IGI Global

Hmotnosť

995

Rozmery

183 x 260 x 27

Kompletný popis

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.

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Automating Software Defect Detection Through Machine Learning and LLMs

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