Automatic and user-assisted test generation: a rough set approach

ABSTRACT

Rough set theoretical concepts have been applied to numerous applications in order to better model the uncertainty and imprecision prevalent in the real world. Enhancements to databases, improved knowledge discovery algorithms, and uncertainty management for spatial data and expert systems are some of the many applications that have benefited from rough set techniques. Rough set techniques can also be incorporated into automatic and user-assisted test generation. This paper discusses the relevant concepts from rough set theory, introduces a test generation system incorporating these rough set concepts, and discusses the benefits that such a system offers in the design and maintenance of tests and test banks.

REFERENCES

• 1: T. Beaubouef, F. Petry, and J. Breckenridge, "Rough Set Based Uncertainty Management for Spatial Databases and Geographical Information Systems," Fourth On-line World Conference on Soft Computing in Industrial Applications (WSC4), Sept. 21--30, 1999.
• 2: T. Beaubouef , R. Lang, Rough set techiques for uncertainty management in automated story generation, Proceedings of the 36th annual Southeast regional conference, p.326-331, April 1998 [doi>10.1145/275295.275379]
• 3: T. Beaubouef, F. Petry, and B. Buckles, "Extension of the Relational Database and its Algebra with Rough Set Techniques," Computational Intelligence, Vol. 11, No. 2, May 1995, pp. 233--245.
• 4: T. Beaubouef and F. Petry, "Rough Querying of Crisp Data in Relational Databases," Third International Workshop on Rough Sets and Soft Computing (RSSC'94), San Jose, California, November 1994.
• 5: Jerzy W. Grzymala-Busse, Managing Uncertainty in Expert Systems, Kluwer Academic Publishers, Norwell, MA, 1900
• 6: J. Komorowski, Z. Pawlak, L. Polkowski, and A. Skowron, "Rough Sets: A Tutorial," in Rough Fuzzy Hybridization: A New Trend in Decision-Making (ed. S. K. Pal and A. Skowron), Springer-Verlag, Singapore, 1999, pp. 3--98.
• 7: Z Pawlak, On conflicts, International Journal of Man-Machine Studies, v.21 n.2, p.127-134, Aug. 1984 [doi>10.1016/S0020-7373(84)80062-0]
• 8: Zdzislaw Pawlak, Rough Sets: Theoretical Aspects of Reasoning about Data, Kluwer Academic Publishers, Norwell, MA, 1992
• 9: R. Slowinski, "A Generalization of the Indiscernibility Relation for Rough Sets Analysis of Quantitative Information," First International Workshop on Rough Sets: State of the Art and Perspectives, Poland, September 1992.
• 10: Padmini Srinivasan, The importance of rough approximation for information retrieval, International Journal of Man-Machine Studies, v.34 n.5, p.657-671, May 1991 [doi>10.1016/0020-7373(91)90017-2]
• 11: Z. M. Wojcik, M. Spinks, B. E. Wojcik, "Application of Rough Sets for Database Mining", Proceedings of 6th International Workshop on Rough Sets, Data Mining and Granular Computing, JCIS-98, Research Triangle Park, NC, October 1998, Vol.2., pp. 327--330.