Abstract
Coverage Directed test Generation (CDG) is a technique that aims to automate
the generation of simulation stimuli based on coverage information. This paper
presents a novel approach to CDG which is based on inductive learning from
examples, in particular Inductive Logic Programming (ILP). The ILP-based CDG
methodology takes tests and associated coverage data, some relational
background knowledge and a coverage task as learning goal. It produces rules
which describe the general structure of tests that achieve the target coverage
task. As a first step a rediscovery experiment has been conducted with the aim
to show that, given a set of pseudo-randomly generated tests together with
their coverage, it is possible to induce rules, at least one per coverage task
reached, that correctly characterize the features of tests to target the
achieved coverage. The success of this rediscovery experiment confirms the
validity of the ILP-based CDG methodology. It establishes the foundations for
automatically closing functional coverage metrics using ILP.
Translated title of the contribution | Towards Inducing Stimulus Generation Directives from Functional Coverage Data |
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Original language | English |
Publisher | University of Bristol |
Publication status | Published - 2005 |