Repository navigation
Create fresh combinatorial member data for each test case - #200
Merged
Merged
Conversation
Co-authored-by: Copilot App <223556219+Copilot@users.noreply.github.com>
This was referenced Aug 26, 2026
This was referenced Sep 4, 2026
This was referenced Sep 11, 2026
This was referenced Sep 25, 2026
This was referenced Oct 2, 2026
This was referenced Oct 9, 2026
This was referenced Oct 23, 2026
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.This suggestion is invalid because no changes were made to the code.Suggestions cannot be applied while the pull request is closed.Suggestions cannot be applied while viewing a subset of changes.Only one suggestion per line can be applied in a batch.Add this suggestion to a batch that can be applied as a single commit.Applying suggestions on deleted lines is not supported.You must change the existing code in this line in order to create a valid suggestion.Outdated suggestions cannot be applied.This suggestion has been applied or marked resolved.Suggestions cannot be applied from pending reviews.Suggestions cannot be applied on multi-line comments.Suggestions cannot be applied while the pull request is queued to merge.Suggestion cannot be applied right now. Please check back later.
CombinatorialMemberDatacurrently shares candidate object instances across generated theory rows, which causes mutable values such as mocks to accumulate state between tests.This change preserves candidate indices while generating combinatorial and pairwise cases, then re-evaluates member data as each row is materialized. Each test case receives a fresh object while retaining the originally selected candidate position and exclusion behavior. Regression coverage verifies both strategies produce unique instances with the expected candidate distribution.
Fixes #39