English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Many methods for automated software test generation, including some thatexplicitly use machine learning (and some that use ML more broadly conceived)derive new tests from existing tests (often referred to as seeds). Often, theseed tests from which new tests are derived are manually constructed, or atleast simpler than the tests that are produced as the final outputs of suchtest generators. We propose annotation of generated tests with a provenance(trail) showing how individual generated tests of interest (especially failingtests) derive from seed tests, and how the population of generated testsrelates to the original seed tests. In some cases, post-processing of generatedtests can invalidate provenance information, in which case we also propose amethod for attempting to construct "pseudo-provenance" describing how the testscould have been (partly) generated from seeds.

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