Computational Intelligence Applications for Software Engineering Problems

Size and complexity of software systems are increasing day by day, and so are the challenges associated with them. Before becoming obsolete, any software undergoes a number of stages during its lifecycle, such as requirements engineering, design, coding, testing, and maintenance to name a few. Each of these stages accommodates a number of costly and error-prone activities that are performed. Computational intelligence techniques can be applied to carry out these activities effectively and efficiently. Computational intelligence techniques are aimed at providing better and optimal solutions to real-world complex optimization problems in reasonable time limit. These are also closely related to artificial intelligence (AI) and incorporate heuristic as well as metaheuristic algorithms. These approaches have successfully been applied to solve real-world problems in various application domains such as healthcare, bioinformatics, civil engineering, computer networks, scheduling, software project planning, resource allocation, and forecasting, to name a few. These techniques have also attracted researchers in the software engineering domain and have been successful in prioritization of requirements, size and cost estimation of software to be developed, software defect prediction and reliability assessment, test case prioritization and vulnerability prediction, and many more. Size of solution space in such domains is very large, and intelligent behavior shown by computational intelligence techniques including evolutionary algorithms, machine learning algorithms, and metaheuristic algorithms find appropriate application of these approaches.Machine learning approaches are, however, constrained by the availability of huge amount of data to extract knowledge and to build and train the model. A metaheuristic algorithm, on the other hand, is a high-level, iterative process. Exploration and exploitation are the two key characteristics of any metaheuristic algorithm that guides... "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."-- Provided by publisher Cover Half Title Title Page Copyright Page About the Editors Table of Contents Contributors Abbreviations Preface Chapter 1: A Statistical Experimentation Approach for Software Quality Management and Defect Evaluations Chapter 2: Open Challenges in Software Measurements Using Machine Learning Techniques Chapter 3: Empirical Software Engineering and Its Challenges Chapter 4: Uncertain Multiobjective COTS Product Selection Problems for Modular Software System and Their Solutions by Genetic Algorithm Chapter 5: Fuzzy Logic Based Computational Technique for Analyzing Software Bug Repository Chapter 6: Software Measurements from Machine Learning to Deep Learning Chapter 7: Time Series Forecasting Using ARIMA Models: A Systematic Literature Review of 2000s Chapter 8: Industry Maintenance Optimization Using AI Chapter 9: Comparative Study of Invasive Weed Optimization Algorithms Chapter 10: An Overview of Computational Tools Chapter 11: Enhanced Intelligence Architecture Chapter 12: Systematic Literature Review of Search-Based Software Engineering Techniques for Code Modularization/Remodularization Chapter 13: Automation of Framework Using DevOps Model to Deliver DDE Software Index 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 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.