A cost model for determining the optimal number of software test cases

A probabilistic model is presented that demonstrates the optimal number of software test cases required in situations where the following can be estimated as independent parameters: (1) the cost per test; (2) the cost per error if undetected until field implementation; (3) the number of software executions over its lifetime; (4) the number of possible different executions; and (5) the number of faults embedded in the software. A formula is derived by the use of calculus and is solved by approximation techniques. Tables of the optimal number of tests over a range of parameter values are presented to illustrate the results. The model serves as a basis for further research efforts to improve the accuracy of input variable estimation