Description
Book SynopsisThis 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 can become 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.
Table of Contents1. A Statistical Experimentation Approach for Software Quality Management and Defect Evaluations 2. Open Challenges in Software Measurements Using Machine Learning Techniques 3. Empirical Software Engineering and Its Challenges 4. Uncertain Multiobjective COTS Product Selection Problems for Modular Software System and Their Solutions by Genetic Algorithm 5. Fuzzy Logic Based Computational Technique for Analyzing Software Bug Repository 6. Software Measurements from Machine Learning to Deep Learning 7. Time Series Forecasting Using ARIMA Models: Systematic Literature Review of 2000s 8. Industry Maintenance Optimization Using AI 9. Comparative Study of Invasive Weed Optimization Algorithms 10. An Overview of Computational Tools 11. Enhanced Intelligence Architecture 12. Systematic Literature Review of Search-Based Software Engineering Techniques for Code Modularization/Remodularization 13. Automation of Framework Using DevOps Model to Deliver DDE Software
