AbstractSoftware validation embodies two notions: fault removal and fault forecasting. Statistical testing involves exercising a piece of software by supplying it with input values that are randomly selected according to a defined probability distribution on its input domain. It can be used as a practical tool for revealing faults in a fault removal phase, and for assessing software dependability in a fault forecasting phase. In both of these, its efficiency is linked to the adequacy of the input probability distribution with respect to the test experiment goal. In this paper a mixed validation strategy combining deterministic and random test data is defined, and the theoretical and experimental work performed to support the strategy is reported. The quoted results relate to the unit testing of four real programs from the nuclear field. They confirm the high fault revealing power of statistical structural testing. Two main directions for further investigation of statistical testing are indicated by the reported work. These are described and solutions to the associated problems are outlined.