Название: Computational Intelligence Applications for Software Engineering ProblemsАвтор: Parma Nand, Nitin Rakesh, Arun Prakash Agrawal
Издательство: CRC Press/Apple Academic Press
Год: 2023
Страниц: 325
Язык: английский
Формат: pdf (true)
Размер: 23.5 MB
This new volume explores the Computational Intelligence (CI) 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.
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 reason able time limit. These are also closely related to artificial Intelligence (AI) and incorporate heuristic as well as metaheuristic algorithms.
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