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Title Computational intelligence and quantitative software engineering / Witold Pedrycz, Giancarlo Succi, Alberto Sillitti, editors.
Imprint Cham : Springer, 2016.

View online
View online
Series Studies in computational intelligence, 1860-949X ; volume 617
Studies in computational intelligence ; v. 617. 1860-949X
Subject Software engineering.
Computational intelligence.
Alt Name Pedrycz, Witold, 1953-
Succi, Giancarlo, 1964-
Sillitti, Alberto,
Description 1 online resource (ix, 207 pages) : illustrations (some color).
Summary 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.
Note English.
Contents Intro; Preface; Contents; 1 The Role of Computational Intelligence in Quantitative Software Engineering; 1 Introduction -- Software Development and the Art of Cappuccino; 2 Persistent Problems in Software Development; 3 Uncertainty; 4 Irreversibility; 5 Complexity; 6 Handling Uncertainty, Irreversibility, and Complexity 2026 and Cappuccino!; 7 The Pivotal Role of Computational Intelligence in Quantitative Software Engineering; 8 Conclusions; References; 2 Computational Intelligence: An Introduction; Abstract; 1 Introduction
2 Computational Intelligence: An Agenda of Synergy of Algorithms of Learning, Optimization and Knowledge Representation3 Neural Networks and Neurocomputing; 4 Evolutionary and Biologically Inspired Computing: Towards a Holistic View at Global Optimization; 5 Information Granularity and Granular Computing; 6 Formal Platforms of Information Granularity; 6.1 Information Granules of Higher Type and Higher Order; 6.2 Hybrid Models of Information Granules; 7 The Concept of Information Granulation-Degranulation; 8 Clustering as a Means of Design of Information Granules
8.1 Unsupervised Learning with Fuzzy Sets8.2 Fuzzy C-Means as an Algorithmic Vehicle of Data Reduction Through Fuzzy Clusters; 8.3 Knowledge-Based Clustering; 9 Computational Intelligence and Software Engineering; 10 Conclusions; References; 3 Towards Benchmarking Feature Subset Selection Methods for Software Fault Prediction; Abstract; 1 Introduction; 2 Related Work; 3 Feature Subset Selection (FSS) Methods; 3.1 Information Gain (IG) Attribute Ranking; 3.2 Relief (RLF); 3.3 Principal Component Analysis (PCA); 3.4 Correlation-Based Feature Selection (CFS)
3.5 Consistency-Based Subset Evaluation (CNS)3.6 Wrapper Subset Evaluation (WRP); 3.7 Genetic Programming (GP); 4 Experimental Setup; 5 Results and Analysis; 6 Validity Evaluation; 7 Conclusions; References; 4 Evolutionary Computation for Software Product Line Testing: An Overview and Open Challenges; Abstract; 1 Introduction; 2 Background; 2.1 SPL Foundations -- Feature Models and Running Example; 2.2 Basics of Evolutionary Algorithms; 3 Overview of SPL Testing; 4 Combinatorial Interaction Testing for Software Product Lines; 4.1 Basic Terminology; 4.2 SPL Genetic Solver (SPLGS)
4.3 State of the Art CIT for SPL Testing5 Multi-objective SPL Testing; 5.1 Multi-objective Optimization Formalization; 5.2 An Example Scenario; 5.3 Computation of Exact Pareto Fronts; 5.4 Sate of the Art in Evolutionary Multi-objective Optimization for SPL Testing; 6 Evolutionary Testing of SPLs in Practice; 7 Open Challenges and Questions; 8 Conclusions; Acknowledgments; References; 5 Metaheuristic Optimisation and Mutation-Driven Test Data Generation; Abstract; 1 Introduction; 2 Test Data Generation; 3 Mutation Analysis; 4 Metaheuristic Optimisation
Note Online resource; title from PDF title page (SpringerLink, viewed January 21, 2016).
ISBN 9783319259642 (electronic bk.)
3319259644 (electronic bk.)
3319259628 (print)
9783319259628 (print)
ISBN/ISSN 10.1007/978-3-319-25964-2
OCLC # 935516454
Additional Format Printed edition: 9783319259628