Computational intelligence applications for software engineering problems / edited by Parma Nand, PhD, Rakesh Nitin, PhD, Arun Prakash Agrawal, PhD, Vishal Jain, PhD.

Includes bibliographical references and index.

A statistical experimentation approach for software quality management and defect evaluations / Alankrita Aggarwal, Kanwalvir Singh Dhindsa, and P. K. Suri -- Open challenges in software measurements using machine learning techniques / Somya Goyal -- Empirical software engineering and its challenges / Sujit Kumar, Spandana Gowda, and Vikramaditya Dave.

"This new volume explores the computational intelligence techniques necessary to carry out different software engineering tasks. Software undergoes various stages before deployment, such as requirements elicitation, software designing, software project planning, software coding, and software testing and maintenance. Every stage is bundled with a number of tasks or activities to be performed. Due to the large and complex nature of software, these tasks become more costly and error prone. This volume aims to help meet these challenges by presenting new research and practical applications in intelligent techniques in the field of software engineering. Computational Intelligence Applications for Software Engineering Problems discusses techniques and presents case studies to solve engineering challenges using machine learning, deep learning, fuzzy-logic-based computation, statistical modeling, invasive weed meta-heuristic algorithms, artificial intelligence, the DevOps model, time series forecasting models, and more. This volume will be helpful to software engineers, researchers, and faculty and advanced students working on intelligent techniques in the field of software engineering"--