Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
System Requirements Engineering
System Requirements Engineering
Requirements Engineering: Processes and Techniques
Requirements Engineering: Processes and Techniques
Software Engineering: Facts and Fallacies
Software Engineering: Facts and Fallacies
A Probabilistic Model for Predicting Software Development Effort
IEEE Transactions on Software Engineering
Software Engineering: (Update) (8th Edition) (International Computer Science)
Software Engineering: (Update) (8th Edition) (International Computer Science)
Predicting software defects in varying development lifecycles using Bayesian nets
Information and Software Technology
A Bayesian belief network for IT implementation decision support
Decision Support Systems
Research Directions in Requirements Engineering
FOSE '07 2007 Future of Software Engineering
Software maintenance project delays prediction using Bayesian Networks
Expert Systems with Applications: An International Journal
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
Improved decision-making for software managers using Bayesian networks
SEA '07 Proceedings of the 11th IASTED International Conference on Software Engineering and Applications
Artificial Intelligence Applications for Improved Software Engineering Development: New Prospects
Artificial Intelligence Applications for Improved Software Engineering Development: New Prospects
An application of uncertain reasoning to requirements engineering
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
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The application of Artificial Intelligence techniques in the processes of Software Engineering is achieving good results in those activities that require the use of expert knowledge. Within Software Engineering, the activities related to requirements become a suitable target for these techniques, since a good or bad execution of these tasks has a strong impact in the quality of the final software product. Hence, a tool to support the decision makers during these activities is highly desired. This work presents a three-layer architecture, which provides a seamless integration between Knowledge Engineering and Requirement Engineering. The architecture is instantiated into a CARE (Computer-Aided Engineering Requirement) tool that integrates some Artificial Intelligence techniques: Requisites, a Bayesian network used to validate the specification of the requirements of a project, and metaheuristic techniques (simulated annealing, genetic algorithm and an ant colony system) to the selection of the requirements that have to be included into the final software product.