Efficient Software Verification: Statistical Testing Using Automated Search

Statistical testing has been shown to be more efficient at detecting faults in software than other methods of dynamic testing such as random and structural testing. Test data are generated by sampling from a probability distribution chosen so that each element of the software's structure is exercised with a high probability. However, deriving a suitable distribution is difficult for all but the simplest of programs. This paper demonstrates that automated search is a practical method of finding near-optimal probability distributions for real-world programs, and that test sets generated from these distributions continue to show superior efficiency in detecting faults in the software.

• Type: Journal
• Year: 2010
• Where: TSE
• Authors: Simon M. Poulding, John A. Clark
• Keywords: Artificial Intelligence, Near-optimal Probability Distributions, Probability Distributions, Software, TSE 2010