Artificial Intelligence Applications for Improved Software Engineering Development: New Prospects
- 8h 27m
- Farid Meziane, Sunil Vadera
- IGI Global
- 2010
Despite decades of research, developing software that is fit for purpose, developed on time, and within budget remains a challenge. Many researchers have advocated the use of artificial intelligence techniques such as knowledge-based systems, neural networks, and data mining as a way of addressing these difficulties.
Artificial Intelligence Applications for Improved Software Engineering Development: New Prospects provides an overview of useful techniques in artificial intelligence for future software development along with critical assessment for further advancement. A compendium of latest industry findings, this Premier Reference Source offers researchers, academicians, and practitioners developmental ideas within the field.
About the Authors
Farid Meziane is a reader in software engineering in the School of Computing, Science and Engineering at the University of Salford (UK). He received a PhD in Software Engineering from the University of Salford (1994). His research interests are in the area of software engineering with particular emphasis on the integration of formal methods in the software development process, software quality and requirements engineering. His research is published in journals that include the Annals of Software Engineering, the Computer Journal, and the Journal of the Operational Research Society. He was awarded the highly commended award from the literati club in 2001 for a paper published in the Integrated Manufacturing Systems Journal. He is on the program committee of many international conferences, is a reviewer for the data and knowledge engineering journal, and serves on the editorial board of the International Journal of Information Technology and Web Engineering. He was the program chair and the organizer of the 9th International Conference on the Application of Natural Language to Information Systems (NLDB04). He is a chartered member of the British Computer Society.
Sunil Vadera is a professor of computer Science and serves as the current Director of the Informatics Research Institute at the University of Salford (UK). He holds a PhD from the University of Manchester in the area of formal methods of software development and is a Fellow of the British Computer Society. His research is driven by the desire to close the gap between theory and practice in artificial intelligence. This has included work on sensor validation with the Mexican Instituto de Electricas, research on the development a system that advises on the relief venting of explosions in chemical processes for the Health and Safety Executive (UK), and research on machine learning for credit rating of sub-prime loans. His research has been published in journals including the Software Engineering Journal, Computer Journal, Formal Aspects of Computing, Foundations of Science, Expert Systems, and IEEE Transactions of Power Systems. He is co-founder of the European Conference on Intelligent Management Systems in Operations, held at Salford since 1997 and has co-edited several special issues of the Journal of Operational Research Society.
In this Book
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Foreword
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Software Project and Quality Modelling Using Bayesian Networks
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Using Bayesian Networks for Web Effort Estimation
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Optimizing Software Development Cost Estimates using Multi—Objective Particle Swarm Optimization
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Auto—Associative Neural Networks to Improve the Accuracy of Estimation Models
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From Textual Scenarios to Message Sequence Charts
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A Bayesian Network for Predicting the Need for a Requirements Review
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Knowledge Engineering Support for Software Requirements, Architectures and Components
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MUSTER—A Situational Tool for Requirements Elicitation
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An Intelligent Computational Argumentation System for Supporting Collaborative Software Development Decision Making
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Supporting Quality—Driven Software Design Through Intelligent Assistants
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Constraint—Based Techniques for Software Testing
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Computational Intelligence for Functional Testing
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Mining Past—Time Temporal Rules—A Dynamic Analysis Approach
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Artificial Intelligence in Software Engineering—Current Developments and Future Prospects
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Compilation of References
