Modular specifications have uses other than verification. Imagine that we have formal interface specifications of all of the components of a system. A formal argument tells us that, if all the component implementations satisfy their specifications, then the system as a whole satisfies its specification. However, we don’t yet have formal proofs that the component implementations are correct.
In this scenario we can use compositional testing to improve our confidence in the system’s correctness. We test each component rigorously against its formal specification. If we have high confidence in the correctness of all of the components, this confidence transfers to the system as a whole because of our formal proof. Put another way, if the system as a whole fails to satisfy its specification, then necessarily one of its components fails its specification and we can discover this fact by component testing.
The question is, how can we test the components rigorously in a way that will give us high confidence of their correctness? One possibility is to use a constrained random approach. That is, we automatically generate test inputs for the component in a way that satisfies its interface assumptions. We then check that the component’s outputs satisfy its interface guarantees. The purpose of randomness is to avoid bias that might creep into a manually generated test suite or testbench.
Ivy can do just that. It can extract a component and a randomized test environment for that component. The test environment generates inputs for the component, calling its exported actions with input parameters that satisfy the component’s assumptions. It also checks that all the component outputs satisfy the component’s guarantees.
This sort of rigorous component-based testing combines the advantages of unit testing and integration testing. Like informal unit testing, the method has the advantage that the component’s inputs can be controlled directly. This gives us much more flexibility to cover the component’s “corner cases” and to expose design errors. Unlike informal unit testing, however, the method uses only the component’s specification, eliminating the possibility of human bias, and giving a definitive reference for evaluating the test results. Like integration testing, compositional testing allows us to gain confidence in the correctness of the system as a whole. Compositional testing can be much faster, however, because it takes many fewer steps to stimulate a component bug through the component’s interface rather than through the system’s interface. In addition, of course, we do not have to execute the entire system to test it compositionally.
In the next few sections, we’ll run through the same examples we looked at with compositional formal verification, but instead we’ll use compositional testing.