Automating Software Defect Detection Through Machine Learning and LLMs. This item is unavailable.Automating Software Defect Detection Through Machine Learning and LLMs

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.

Pancham Singh is currently working as an Assistant Professor in the Department of Information Technology at AKGEC, Ghaziabad since 2007. Mr. Singh has over 18 years of teaching and one year of industry experiences. Mr. Singh received a B.Tech. Degree in Computer Science & Engineering from Dr. A.P.J. Abdul Kalam Technical University (formerly, UPTU), Lucknow, Uttar Pradesh, India in 2005; a Master Degree in Information Technology from RTU, Kota, Rajasthan, India in 2013 and a Pursuing PhD from Netaji Subhas University of Technology (NSUT), New Delhi, India since january 2023. In addition he has authored 3 books in Computer Science. He has presented and published more than 40 papers in international journals and conferences. He has reviewed more 50 papers for the International Journals and Conferences. In addition he has published 20 National and International Patents and 3 Design Grants. He was the session chair for the International Conference ICDT 2024. In addition, He did work as a time table In-charge since 2010 to 2023 for more than 13 yrs also media In-charge since 2015 to 2023 for more than 8 yrs in AKGEC. He did Flying Squad Duty assigned by AKTU as a In-charge and team member 4 times. He has attended more than 30 FDPs and did work as a In-charge and member for NBA and NACC in AKGEC. His research interests are Machine Learning, Deep Learning, Blockchain, Internet of Things, and Software Engineering.