Requirements Analysis: Evaluating KAOS Models
ABSTRACT
Wigmore’s charts and Bayesian networks are used to represent graphically the construction of arguments and to evaluate them. KAOS is a goal oriented requirements analysis method that enables the analysts to capture requirements through the realization of the business goals. However, KAOS does not have inbuilt mechanism for evaluating these goals and the inferring process. This paper proposes a method for evaluating KAOS models through the extension of Wigmore’s model with features of Bayesian networks.
Wigmore’s charts and Bayesian networks are used to represent graphically the construction of arguments and to evaluate them. KAOS is a goal oriented requirements analysis method that enables the analysts to capture requirements through the realization of the business goals. However, KAOS does not have inbuilt mechanism for evaluating these goals and the inferring process. This paper proposes a method for evaluating KAOS models through the extension of Wigmore’s model with features of Bayesian networks.
Cite this paper
nullF. Almisned and J. Keppens, "Requirements Analysis: Evaluating KAOS Models," Journal of Software Engineering and Applications, Vol. 3 No. 9, 2010, pp. 869-874. doi: 10.4236/jsea.2010.39101.
nullF. Almisned and J. Keppens, "Requirements Analysis: Evaluating KAOS Models," Journal of Software Engineering and Applications, Vol. 3 No. 9, 2010, pp. 869-874. doi: 10.4236/jsea.2010.39101.
References
[1] A. Dardenne, A. van Lamsweerde and S. Fickas, “Goal-Directed Requirements Acquisition,” Science of Computer Programming, Vol. 20, No. 1-2, April 1993, pp. 3-50. [2] Respect IT, A KAOS Tutorial, Objectiver, 2007. [3] F. Taroni, C. Aitken, P. Garbolino and A. Biedermann, “Bayesian Networks and Probabilistic Inference in Fo-rensic Science,” John Wiley and Sons, Chichester, 2006. [4] E. Charniak, “Bayesian Networks without Tears,” AI Magazine, Vol. 12, No. 4, 1991, pp. 50-63. [5] R. Neapolitan, “Learning Bayesian Networks,” Prentice Hall, New Jersey, 2004. [6] C. Reed, D. Walton and F. Macagno, “Argument Diagramming in Logic, Law and Artificial Intelligence,” The Knowledge Engineering Review, Vol. 22, No. 1, 2007, pp. 87-109. [7] D. A. Schum, “A Wigmorean Interpretation of the Evaluation of a Complicated Pattern of Evidence,” Technical Report, 2005. http://tinyurl.com/2a3elq [8] A. Hepler, P. Dawid and V. Leucari, “Object Oriented Graphical Representations of Complex Patterns of Evidence,” Law, Probability and Risk, Vol. 6, No. 1-4, 2007, pp. 275-293.
[1] A. Dardenne, A. van Lamsweerde and S. Fickas, “Goal-Directed Requirements Acquisition,” Science of Computer Programming, Vol. 20, No. 1-2, April 1993, pp. 3-50. [2] Respect IT, A KAOS Tutorial, Objectiver, 2007. [3] F. Taroni, C. Aitken, P. Garbolino and A. Biedermann, “Bayesian Networks and Probabilistic Inference in Fo-rensic Science,” John Wiley and Sons, Chichester, 2006. [4] E. Charniak, “Bayesian Networks without Tears,” AI Magazine, Vol. 12, No. 4, 1991, pp. 50-63. [5] R. Neapolitan, “Learning Bayesian Networks,” Prentice Hall, New Jersey, 2004. [6] C. Reed, D. Walton and F. Macagno, “Argument Diagramming in Logic, Law and Artificial Intelligence,” The Knowledge Engineering Review, Vol. 22, No. 1, 2007, pp. 87-109. [7] D. A. Schum, “A Wigmorean Interpretation of the Evaluation of a Complicated Pattern of Evidence,” Technical Report, 2005. http://tinyurl.com/2a3elq [8] A. Hepler, P. Dawid and V. Leucari, “Object Oriented Graphical Representations of Complex Patterns of Evidence,” Law, Probability and Risk, Vol. 6, No. 1-4, 2007, pp. 275-293.