Random number generation and quasi-Monte Carlo methods
Random number generation and quasi-Monte Carlo methods
Test template framework: a specification-based testing case study
ISSTA '93 Proceedings of the 1993 ACM SIGSOFT international symposium on Software testing and analysis
Quasi-random sequences and their discrepancies
SIAM Journal on Scientific Computing
The art of computer programming, volume 2 (3rd ed.): seminumerical algorithms
The art of computer programming, volume 2 (3rd ed.): seminumerical algorithms
Estimation of software reliability by stratified sampling
ACM Transactions on Software Engineering and Methodology (TOSEM)
Algorithm 647: Implementation and Relative Efficiency of Quasirandom Sequence Generators
ACM Transactions on Mathematical Software (TOMS)
Algorithm 823: Implementing scrambled digital sequences
ACM Transactions on Mathematical Software (TOMS)
Toward a theory of test data selection
Proceedings of the international conference on Reliable software
Evaluation of Three Specification-Based Testing Criteria
ICECCS '00 Proceedings of the 6th IEEE International Conference on Complex Computer Systems
Using Software Architecture for Code Testing
IEEE Transactions on Software Engineering
Test-driven specification: paradigm and automation
Proceedings of the 44th annual Southeast regional conference
On the optimal Halton sequence
Mathematics and Computers in Simulation
Generating test data for specification-based tests via quasirandom sequences
ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part IV
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This paper presents work on generation of specification-driven test cases based on quasirandom (low-discrepancy) sequences instead of pseudorandom numbers. This approach is novel in software testing. This enhanced uniformity of quasirandom sequences leads to faster generation of test cases covering all possibilities. We demonstrate by examples that quasirandom sequences can be a viable alternative to pseudorandom numbers in generating test cases. In this paper, we present a method that can generate test cases 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. The use of different quasirandom sequences for generating test cases is presented in this paper.