Design full test-space scenarios at once and delegate precised evidence-based exploratory testing to your AI

Description and use

The combinatorial framework models variations, action sequences, and events to generate bounded test cases and evidence, enabling detection of hidden bugs.

Problem

Testing systems for unknown bugs is hard because bugs are interactions of settings, actions, and events that are hard to enumerate.

Outcome

The framework generated 72 cases, 48 executable, yielding 44 PASS and 4 DOMAIN_FAIL, demonstrating its ability to detect planted defects in both in-JVM and browser scenarios.

Workflow

• Define variation slots (e.g., customer type, payment method, channel, pricing policy).
• Create action sequences and optional events (e.g., submit, timeout, retry).
• Write a JSON scenario file (e.g., first-scenario.json) and properties file (first-scenario.properties).
• Run the framework commands: validate, plan, and candidates to generate and inspect test candidates.
• Review the generated candidates and evidence before executing tests.
• Refine the model based on findings and repeat the process.

Evidence

Combinatorics-Framework-JVM turns a model of settings, action histories and optional events into planned test cases, and keeps the experiment inspectable from design through evidence.

Source

Validate, inspect the plan, read the candidates, all before anything runs. These are plain-text candidates for learning the model, not programs to execute.

Source

The full run ends with 44 PASS and 4 DOMAIN_FAIL, with no harness failures.

Source