Full Description
In a down-to-the earth manner, the volume lucidly presents how the fundamental concepts, methodology, and algorithms of Computational Intelligence are efficiently exploited in Software Engineering and opens up a novel and promising avenue of a comprehensive analysis and advanced design of software artifacts. It shows how the paradigm and the best practices of Computational Intelligence can be creatively explored to carry out comprehensive software requirement analysis, support design, testing, and maintenance.
Software Engineering is an intensive knowledge-based endeavor of inherent human-centric nature, which profoundly relies on acquiring semiformal knowledge and then processing it to produce a running system. The knowledge spans a wide variety of artifacts, from requirements, captured in the interaction with customers, to design practices, testing, and code management strategies, which rely on the knowledge of the running system. This volume consists of contributions written by widely acknowledged experts in the field who reveal how the Software Engineering benefits from the key foundations and synergistically existing technologies of Computational Intelligence being focused on knowledge representation, learning mechanisms, and population-based global optimization strategies.
This book can serve as a highly useful reference material for researchers, software engineers and graduate students and senior undergraduate students in Software Engineering and its sub-disciplines, Internet engineering, Computational Intelligence, management, operations research, and knowledge-based systems.
Contents
The Role of Computational Intelligence in Quantitative Software Engineering.- Computational Intelligence: An Introduction.- Towards Benchmarking Feature Subset Selection Methods for Software Fault Prediction.- Evolutionary Computation for Software Product Line Testing: An Overview and Open Challenges.- Metaheuristic Optimisation and Mutation-Driven Test Data Generation.- Measuring the Utility of Functional-Based Software Using Centroid-Adjusted Class Labelling.- Toward Accurate Software Effort Prediction Using Multiple Classifier Systems.- Complex Specifications Requirements Software Quantitative.- Possibilistic Assessment of Process-Related Disclosure Risks on the Cloud.
