In the context of structural testing, automatic test-pattern generation (ATPG) may fail to provide suites covering 100% of the testing requirements for grey-box programs, i.e., Applications wherein source code is available for some parts (white-box), but not for others (black-box). Furthermore, test suites based on abstract models may elicit behaviors on the actual program that diverge from the intended ones. In this paper, we present a new ATPG methodology to reduce divergence without increasing manual effort. This is achieved by (i) learning models of black-box components as finite-state machines, and (ii) composing the learnt models with the white-box components to generate test-suites for the grey-box program. Experiments with a prototypical implementation of our methodology show that it yields measurable improvements over two comparable state-of-the-art solutions.
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