Title: Provenance and Pseudo-Provenance for Seeded Learning-Based Automated Test Generation,
Abstract: Many methods for automated software test generation, including some that
explicitly use machine learning (and some that use ML more broadly conceived)
derive new tests from existing tests (often referred to as seeds). Often, the
seed tests from which new tests are derived are manually constructed, or at
least simpler than the tests that are produced as the final outputs of such
test generators. We propose annotation of generated tests with a provenance
(trail) showing how individual generated tests of interest (especially failing
tests) derive from seed tests, and how the population of generated tests
relates to the original seed tests. In some cases, post-processing of generated
tests can invalidate provenance information, in which case we also propose a
method for attempting to construct "pseudo-provenance" describing how the tests
could have been (partly) generated from seeds.