Computational Intelligence Applications for Software Engineering Problems

This volume explores computational intelligence techniques for software engineering tasks, addressing cost and error issues with new research and practical applications in machine learning, deep learning, fuzzy logic, statistical modeling, invasive weed meta-heuristic algorithms, artificial intelligence, DevOps, and time series forecasting models.

Format: Hardback
Length: 304 pages
Publication date: 10 February 2023
Publisher: Apple Academic Press Inc.

Software engineering is a complex and challenging field that involves various stages, including requirements elicitation, software designing, software project planning, software coding, and software testing and maintenance. These stages are bundled with a number of tasks or activities that can become costly and error prone due to the large and complex nature of software. 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.

One of the key challenges in software engineering is the need to develop software that is reliable, efficient, and scalable. Machine learning and deep learning are two techniques that can be used to achieve this goal. Machine learning involves training computers to learn from data, while deep learning involves training computers to learn from data in a more complex way. Both techniques can be used to develop software that can make predictions, classify data, and optimize processes.

Deep learning has been used in a variety of software engineering applications, including image recognition, natural language processing, and autonomous vehicles. For example, deep learning has been used to develop self-driving cars that can navigate through traffic without human intervention. Deep learning has also been used to develop medical imaging software that can detect cancer cells.

Fuzzy-logic-based computation is another technique that can be used to develop software that is more flexible and adaptable. Fuzzy logic involves using fuzzy sets to represent uncertain information and make decisions based on that information. Fuzzy logic has been used in a variety of software engineering applications, including decision-making systems, control systems, and robotics.

Statistical modeling is another technique that can be used to develop software that is more accurate and reliable. Statistical modeling involves using statistical techniques to analyze data and make predictions. Statistical modeling has been used in a variety of software engineering applications, including weather forecasting, financial analysis, and medical research.

Invasive weed meta-heuristic algorithms are another technique that can be used to develop software that is more efficient and effective. Invasive weed meta-heuristic algorithms involve using algorithms to find solutions to complex problems. Invasive weed meta-heuristic algorithms have been used in a variety of software engineering applications, including optimization, scheduling, and resource allocation.

Artificial intelligence is another technique that can be used to develop software that is more intelligent and autonomous. Artificial intelligence involves using computers to perform tasks that would normally require human intelligence. Artificial intelligence has been used in a variety of software engineering applications, including robotics, natural language processing, and autonomous vehicles.

The DevOps model is another technique that can be used to develop software that is more efficient and effective. The DevOps model involves combining development and operations into a single process. The DevOps model has been used in a variety of software engineering applications, including web development, software development, and cloud computing.

Time series forecasting models are another technique that can be used to develop software that is more accurate and reliable. Time series forecasting models involve using statistical techniques to analyze data and make predictions about future events. Time series forecasting models have been used in a variety of software engineering applications, including weather forecasting, financial analysis, and medical research.

In conclusion, computational intelligence techniques are essential for carrying out different software engineering tasks. 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 can be used to develop software that is reliable, efficient, and scalable. This volume aims to help meet these challenges by presenting new research and practical applications in intelligent techniques in the field of software engineering.

Weight: 654g
Dimension: 236 x 159 x 22 (mm)
ISBN-13: 9781774910467