Generating test data for specification-based tests via quasirandom sequences

Authors:
Hongmei Chi;Edward L. Jones;Deidre W. Evans;Martin Brown
Affiliations:
School of Computational Science, Florida State University, Tallahassee, FL;Department of Computer and Information Sciences, Florida A& M University, Tallahassee, FL;Department of Computer and Information Sciences, Florida A& M University, Tallahassee, FL;Department of Computer and Information Sciences, Florida A& M University, Tallahassee, FL
Venue:
ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part IV
Year:
2006

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Abstract

This paper presents work on generation of specification- driven test data, by introducing techniques based on a subset of quasirandom sequences (completely uniformly distributed sequences) to generate test data. This approach is novel in software testing. This enhanced uniformity of quasirandom sequences leads to faster generation of test data covering all possibilities. We demonstrate by examples that well-distributed sequences can be a viable alternative to pseudorandom numbers in generating test data. In this paper, we present a method that can generate test data from a decision table specification more effectively via quasirandom numbers. Analysis of a simple problem in this paper shows that quasirandom sequences achieve better data than pseudorandom numbers, and have the potential to converge faster and so reduce the computational burden. Functional test coverage, an objective criteria, evaluates the quality of a test set to ensure that all specified behaviors will be exercised by the test data.