Agentic framework for integration, end2end, multiuserinteraction, security testing based on usecases.
tegwick
eee7722714
A stable agentic realization becomes deterministic code. All four exit criteria met; 163 tests pass. - crystallization.py: trajectory capture, stability assessment requiring the same path across several runs, CrystallizedDriver, pytest codegen - crystallized/test_grant_access.py: generated, runs with no model, carries its lineage in the docstring - descendant preserves the ancestor's oracle set, agrees with it across five lab versions, and still catches a seeded defect - reversibility shown both ways via new M24 (grant endpoint renamed): the frozen descendant fails loudly rather than searching, and the agentic ancestor recovers from the same mutation F-0007 (open): the 54% cost reduction must not be quoted in support of the thesis. The T07 runtime is token-free, so the measured saving is one page fetch, one parse and a two-candidate scoring pass. The saving the concept actually claims - tokens, latency, retry variance - is unmeasured. Together with F-0005 this makes a bounded live-model experiment the highest-value next investment. Assertions in the generated test are imported rather than restated, so it is not fully standalone. Deliberate: paraphrased claims would be a second unverified statement of intent. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> Assistant: claude-code Assistant-Model: opus Assistant-Process: 1629012@bnt-lap001 Assistant-Session: 78d4fb13-8a1e-474b-87a3-9b9261c49a39 |
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|---|---|---|
| crystallized | ||
| docs | ||
| history | ||
| lab | ||
| research | ||
| scenarios | ||
| src/testdriver | ||
| tests | ||
| workplans | ||
| .custodian-brief.md | ||
| .gitignore | ||
| .repo-classification.yaml | ||
| AGENTS.md | ||
| INTENT.md | ||
| pyproject.toml | ||
| README.md | ||
| SCOPE.md | ||
| WORK-RECORDS.md | ||
test-driver
Agentic framework for integration, end-to-end, multi-user interaction and security testing, driven by use cases.
Tests mature alongside the software they protect: fluid and agentic while
behaviour is changing, deterministic once it settles. See INTENT.md for the
thesis and SCOPE.md for boundaries.
Status: research prototype. The deterministic kernel runs; agentic
realization, adaptation classification and crystallization are not built yet.
Current work: workplans/TD-WP-0002-vertical-spike-crystallization.md.
Run
python3 -m pytest -q # the whole suite
python3 -m pytest -q tests/test_reference_scenario.py
No third-party dependencies. Python ≥ 3.11, pytest for the suite.
Layout
src/testdriver/ the kernel — intent, world, actions, drivers,
observers, oracles, evidence, runner
lab/ the system under test
scenarios/ reference scenarios
research/ hypotheses, experiments, findings, fitness map
docs/ concept model, improvement loop, milestones, design notes
history/ assessments and completed-work write-ups
Reading order
INTENT.md— the thesisdocs/TestDriverConceptModel.md— canonical concept setdocs/TestDriverClassificationDesign.md— why adaptation cannot normalize a defect, and where model judgment is and is not permitteddocs/TestDriverInitialMilestones.md— canonical milestones M0–M10