Provenance and Pseudo-Provenance for Seeded Learning-Based Automated Test Generation

TL;DR

This paper introduces methods for annotating generated software tests with provenance information to trace their origins from seed tests, including pseudo-provenance when original provenance is lost, enhancing understanding of test generation processes.

Contribution

It proposes a novel approach to annotate generated tests with provenance and pseudo-provenance, improving traceability in seed-based automated test generation.

Findings

Provenance annotation helps trace test origins.

Pseudo-provenance can be constructed when original provenance is invalid.

Enhances understanding of test generation processes.

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.