Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Abran, A., Moore, J., Bourque, P., Dupuis, R., Tripp, L.: Guide to the Software Engineering Body of Knowledge 2004 Version. IEEE Computer Society, Los Alamitos (2004)
Barry, P.S., Laskey, K.B.: An Application of Uncertain Reasoning to Requirements Engineering. In: 15th Conference on Uncertainty in Artificial Intelligence, pp. 41–48. Morgan Kaufmann, Stockholm (1999)
Standish Group: Chaos Report. Technical report, Standish Group International (1994)
Johnson, J.: CHAOS chronicles v3.0. Technical report, Standish Group International (2003)
Cheng, B.H., Atlee, J.M.: Research directions in requirements engineering. In: Future of Software Engineering, FOSE 2007, pp. 285–303. Institute of Electrical and Electronics Engineers, Minneapolis (2007)
Elvira Consortium: Elvira: An environment for probabilistic graphical models. In: First International Workshop on Probabilistic Graphical Models (PGM 2002), Cuenca, España, pp. 222–230 (2002), http://leo.ugr.es/elvira/
Fenton, N., Neil, M., Marsh, W., Hearty, P., Marquez, D., Krause, P., Mishra, R.: Predicting software defects in varying development lifecycles using Bayesian nets. Information and Software Technology 49(1), 32–43 (2007)
Glass, A.R.L.: Facts and Fallacies of Software Engineering. Pearson Education, Inc., Boston (2002)
Jensen, F.V.: Bayesian Networks and decision graphs. Springer, New York (2001)
Jensen, F.V., Nielsen, T.: Bayesian networks and decision graphs. Springer, New York (2007)
Kotonya, G., Sommerville, I.: Requirements Engineering: Processes and Techniques. Wiley (1998)
Lauria, E.J., Duchessi, P.J.: A Bayesian Belief Network for IT implementation decision support. Decision Support Systems 42(3), 1573–1588 (2006)
Loucopoulos, P., Karakostas, V.: System Requirements Engineering. McGraw-Hill, Inc., New York (1995)
de Melo, A.C., Sanchez, A.J.: Software maintenance project delays prediction using Bayesian Networks. Expert Systems with Applications 34(2), 908–919 (2008)
Meziane, F., Vadera, S. (eds.): Artificial intelligence applications for improved software engineering development: new prospects. IGI Global, Hershey (2010)
Orellana, F.J., Cañadas, J., del Águila, I.M., Túnez, S.: INSCO requisite - a Web-Based RM-Tool to support hybrid software development. In: International Conference of Enterprise Information System ICEIS, Barcelona, Spain, vol. (3-1), pp. 326–329 (2008)
Pearl, J.: Probabilistic reasoning in intelligent systems: networks of plausible inference. Morgan Kaufman, San Mateo (1988)
Pendharkar, P., Pendharkar, P., Subramanian, G., Rodger, J.: A probabilistic model for predicting software development effort. IEEE Transactions on Software Engineering 31(7), 615–624 (2005)
Radlinski, L., Fenton, N., Neil, M.: Improved Decision-Making for Software Managers Using Bayesian Networks. In: 11th IASTED Int. Conf. Software Engineering and Applications (SEA), pp. 13–19. Acta Press, Cambridge (2007)
del Sagrado, J., del Águila, I.M.: A Bayesian Network for Predicting the Need for a Requirements Review. In: Meziane, F., Vadera, S. (eds.) Artificial Intelligence Applications for Improved Software Engineering Development: New Prospects, pp. 106–128. IGI Global, Hershey (2010)
del Sagrado, J., del Águila, I.M., Orellana, F.J.: Requirement selection: Knowledge based optimization techniques for solving the next release problem. In: 6th Workshop on Knowledge Engineering and Software Engineering (KESE 2010), pp. 40–51. CEUR-WS, Karlsruhe (2010)
Sommerville, I.: Software Engineering. Addison-Wesley Longman Publishing Co., Inc., Boston (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
del Sagrado, J., del Águila, I.M., Orellana, F.J. (2011). Architecture for the Use of Synergies between Knowledge Engineering and Requirements Engineering. In: Lozano, J.A., Gámez, J.A., Moreno, J.A. (eds) Advances in Artificial Intelligence. CAEPIA 2011. Lecture Notes in Computer Science(), vol 7023. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25274-7_22
Download citation
DOI: https://doi.org/10.1007/978-3-642-25274-7_22
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-25273-0
Online ISBN: 978-3-642-25274-7
eBook Packages: Computer ScienceComputer Science (R0)