Evolutionary Computation and Optimization Algorithms in Software Engineering: Applications and Techniques: Applications and Techniques

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Chis, Monica
IGI Global, Jun 30, 2010 - Education - 282 pages

Evolutionary computation brings new tools for software engineering by informing the systematic, disciplined, quantifiable processes of developing, operating, and maintaining software.

Evolutionary Computation and Optimization Algorithms in Software Engineering: Applications and Techniques lays the foundation for the successful integration of evolutionary computation into software engineering. It surveys techniques ranging from genetic algorithms, to swarm optimization theory, to ant colony optimization, demonstrating their uses and capabilities. These techniques are applied to aspects of software engineering such as software testing, quality assessment, reliability assessment, and fault prediction models, among others, to providing researchers, scholars and students with the knowledge needed to expand this burgeoning application.


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A Survey of the Evolutionary Computation Techniques for Software Engineering
Using Evolutionary Based Approaches to Estimate Software Development Effort
The Application of Genetic Algorithms to the Evaluation of Software Reliability
Synthesis of ObjectOriented Software Structural Models Using Quality Metrics and CoEvolutionary Genetic Algorithms
Application of Artificial Immune Systems Paradigm for Developing Software Fault Prediction Models
Genetic Programming for CrossRelease Fault Count Predictions in Large and Complex Software Projects
Exploring a Self Organizing Multi Agent Approach for Service Discovery
Innovative Hybrid Genetic Algorithms and Line Search Method for Industrial Production Management
Automatic Test Sequence Generation for State Transition Testing via Ant Colony Optimization
Object Oriented Software Testing with Genetic Programming and Program Analysis
A Fuzzy MultiCriteria Approach
Verification of Attributes in Linked Lists Using Ant Colony Metaphor
Compilation of References
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About the author (2010)

Monica Chis received her BS degree in Computer Science from Babes-Bolyai University, Cluj-Napoca, Romania in 1995. She is currently Project Manager at SIEMENS IT Solutions and Services, Romania. She was working in a private University in Cluj-Napoca, Romania as Senior Lecturer. Her research activities focuses on data mining, computational intelligence and swarm intelligence, applications of evolutionary computation in data mining, software engineering, software engineering and evolutionary computation, e-learning, time series data mining, social networks. She has published research articles in book chapters and conference proceedings. She served on the program committee for international conferences and she is additional reviewer for international conferences and journals. She has some important collaboration with foreign companies in the field of project management, research and data analysis. [Editor]

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