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.
Cite
@article{arxiv.1711.01661,
title = {Provenance and Pseudo-Provenance for Seeded Learning-Based Automated Test Generation},
author = {Alex Groce and Josie Holmes},
journal= {arXiv preprint arXiv:1711.01661},
year = {2017}
}
Comments
Presented at NIPS 2017 Symposium on Interpretable Machine Learning