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<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2w3awgokqne6te4nvlofavy5a4" style="color: black;">Lecture Notes in Computer Science</a>
Testing a black-box system without recourse to a specification is difficult, because there is no basis for estimating how many tests will be required, or to assess how complete a given test set is. Several researchers have noted that there is a duality between these testing problems and the problem of inductive inference (learning a model of a hidden system from a given set of examples). It is impossible to tell how many examples will be required to infer an accurate model, and there is no<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-642-24580-0_15">doi:10.1007/978-3-642-24580-0_15</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/wwqbvfxw3ngvnm4uihrk5zyyly">fatcat:wwqbvfxw3ngvnm4uihrk5zyyly</a> </span>
more »... for telling how complete a given set of examples is. These issues have been addressed in the domain of inductive inference by developing statistical techniques, where the accuracy of an inferred model is subject to a tolerable degree of error. This paper explores the application of these techniques to assess test sets of black-box systems. It shows how they can be used to reason in a statistically justified manner about the number of tests required to fully exercise a system without a specification, and how to provide a valid adequacy measure for black-box test sets in an applied context.
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