Boosting Software Development using Machine Learning

This book explores the transformative effects of AI and ML on software engineering. It emphasizes the potential of cutting-edge software development technologies such as Generative AI and ML applications. This book incorporates data-driven strategies across the entire software development life cycle, from requirements elicitation and design to coding, testing, and deployment. It illustrates the evolution from traditional frameworks to agile and DevOps methodologies. The potential of Generative AI for automating repetitive tasks and enhancing code quality is highlighted, along with ML applications in optimizing testing, effort estimation, design pattern recognition, fault prediction, debugging, and security through anomaly detection. These techniques have significantly improved software development efficiency, predictability, and project management effectiveness. While remarkable progress has been made, much remains to be done in this evolving area. This edited book is a timely effort toward advancing the field and promoting interdisciplinary collaboration in addressing ethical, security, and technical challenges.

List of contents

1.Transforming Software Development: From Traditional Methods to Generative Artificial Intelligence.- 2.Case Study: Transforming Operational and Organizational Efficiency Using Artificial Intelligence and Machine Learning.- 3.Revolutionizing Software Development: The Transformative Influence of Machine Learning Integrated SDLC Model.- 4.Generative Coding: Unlocking Ontological AI.- 5.Case Studies: Machine Learning Approaches for Software Development Effort Estimation.- 6.Hybridizing Metaheuristics and Analogy-based Methods with Ensemble Learning for Improved Software Cost Estimation.- 7.A Review on Detection of Design Pattern in Source Code Using Machine Learning Techniques.- 8.Machine Learning Techniques for the Measurement of Software Attributes.- 9.An Effective Analysis of New Metaheuristic Algorithms and its Performance Comparison.- 10.Empowering Software Security: Leveraging Machine Learning for Anomaly Detection and Threat Prevention.- 11.Sentiment Analysis on Movie Reviews Using the Convolutional LSTM (Co-LSTM) Model.- 12.An Overview of AI Workload Optimization Techniques.- 13.Opportunity Discovery for Effective Innovation Using Artificial Intelligence.- 14.Applications of Machine Learning Algorithms in Open Innovation.

About the author

Tirimula Rao Benala is an assistant professor of Information Technology at JNTUGV College of Engineering Vizianagaram (Autonomous), Jawaharlal Nehru Technological University Gurajada Vizianagaram, Vizianagaram-535003, India. He received his Ph.D. in 2020 from the Jawaharlal Nehru Technological University in Kakinada, India. He was at the Center for Theoretical Studies, Indian Institute of Technology Kharagpur, as a visiting scholar in 2017. He was a visiting fellow at Chicago State University in 2018 under the US-India 21st Century Knowledge Initiative grant. He is a senior member of IEEE and CSI. He is a Life member of I.E. (I) and IETE. He is a professional member of ACM. He has published about 30 research papers in refereed journals and conferences. He has guided 75 postgraduate students and currently guides seven postgraduate students and four doctoral students. He has twenty years of teaching experience.
Satchidananda Dehuri (Senior Member IEEE) has been a professor in the Department of Computer Science (Erstwhile Department of Information and Communication Technology), Fakir Mohan University, Balasore, Odisha, India since 2013. Before this appointment, for a short stint (i.e., from Oct. 2012 to May 2014), he was an associate professor in the Department of Systems Engineering at Ajou University, South Korea. He received his M.Tech. and Ph.D. in Computer Science from Utkal University, Vani Vihar, Odisha, in 2001 and 2006, respectively. He visited as a BOYSCAST Fellow to the Soft Computing Laboratory, Yonsei University, Seoul, South Korea, under the BOYSCAST Fellowship Program of DST, Govt. of India in 2008. In 2010, he received the Young Scientist Award in Engineering and Technology for the year 2008 from Odisha Vigyan Academy, Department of Science and Technology, Govt. of Odisha.
Rajib Mallobtained his professional degrees, Bachelor’s, Master’s, and Ph.D. from the Indian Institute of Science, Bangalore. He has been working as a faculty in the Department of Computer Science and Engineering at IIT, Kharagpur, for the last 30 years. He has published about 150 refereed journal and conference papers. His main research interests are program analysis and testing.
 Dr. Margarita N. Favorskaya is a professor and head of the Department of Informatics and Computer Techniques at Reshetnev Siberian State University of Science and Technology. Russian Federation. Professor Favorskaya has been a member of the KES organization since 2010, an IPC member, and the Chair of invited sessions of over 30 international conferences. She is the author or the co-author of 200 publications and 20 educational manuals in computer science. She co-authored/co-edited around 20 books/conference proceedings for Springer in the last 10 years. She supervised nine Ph.D. candidates and is presently supervising four Ph.D. students. Her main research interests are digital image and video processing, remote sensing, pattern recognition, fractal image processing, artificial intelligence, and information technologies.
 

Summary

This book explores the transformative effects of AI and ML on software engineering. It emphasizes the potential of cutting-edge software development technologies such as Generative AI and ML applications. This book incorporates data-driven strategies across the entire software development life cycle, from requirements elicitation and design to coding, testing, and deployment. It illustrates the evolution from traditional frameworks to agile and DevOps methodologies. The potential of Generative AI for automating repetitive tasks and enhancing code quality is highlighted, along with ML applications in optimizing testing, effort estimation, design pattern recognition, fault prediction, debugging, and security through anomaly detection. These techniques have significantly improved software development efficiency, predictability, and project management effectiveness. While remarkable progress has been made, much remains to be done in this evolving area. This edited book is a timely effort toward advancing the field and promoting interdisciplinary collaboration in addressing ethical, security, and technical challenges.

Product details

• Assisted by: Tirimula Rao Benala (Editor), Satchidanand Dehuri (Editor), Margarita N. Favorskaya (Editor), Rajib Mall (Editor), Rajib Mall et al (Editor)
• Publisher: Springer, Berlin
• Languages: English
• Product format: Hardback
• Release: 07.06.2025
• EAN: 9783031881879
• ISBN: 978-3-0-3188187-9
• No. of pages: 318
• Illustrations: XVI, 318 p. 65 illus., 38 illus. in color.
• Series: Artificial Intelligence-Enhanced Software and Systems Engineering
• Subjects: Natural sciences, medicine, IT, technology > Technology > General, dictionaries; Innovation, Empowerment, machine learning, Maschinelles Lernen, Technology, Creativity, Artificial Intelligence, software development, Efficiency, Quality, Computational Intelligence, Ethical Considerations, Real-world Case Studies, Industry Elevation, Professional Guidance, Future Outlook