testaxiom 0.1.0

AI-Powered Test Design Engine — from requirements to mathematically optimal test cases using ISTQB methodology

TestAxiom

From requirements to mathematically optimal test cases — with AI intelligence and ISTQB methodology.

TestAxiom is an AI-Powered Test Design Engine that combines Large Language Model intelligence with deterministic, mathematically-grounded test design techniques.

Why TestAxiom?

Most AI test generators just throw requirements at an LLM and say "generate tests." The result: bloated, unexplainable, untraceable test suites. Mathematical tools like PICT are precise but require manual parameter extraction.

TestAxiom bridges the gap. Every test case comes with:

• The technique that generated it (EP, BVA, Decision Table, Pairwise)
• The mathematical rationale explaining WHY this specific value was chosen
• Full traceability from requirement → technique → test case

Quick Start

pip install testaxiom

Python API

from testaxiom import analyze

result = analyze("age", param_type="int", valid_range=(18, 65))
print(result.summary())

CLI

testaxiom --param age --type int --range 18 65
testaxiom --param age --type int --range 18 65 --bva-mode 3-value --json

Supported Techniques

Technique	Status	Description
Equivalence Partitioning (EP)	✅ Ready	Divides input into partitions with equivalent behavior
Boundary Value Analysis (BVA)	✅ Ready	Tests at partition boundaries (2-value & 3-value)
Decision Table	🔜 Next	Covers combinations of business rules
State Transition	🔜 Planned	Tests state machine transitions
Pairwise (All-Pairs)	🔜 Planned	Minimizes combinatorial test sets

Architecture

testaxiom/
├── core.py          # Data models (ParameterSpec, TestCase, AnalysisResult)
├── engines/         # Deterministic technique engines (no AI dependency)
│   ├── equivalence_partitioning.py
│   ├── boundary_value.py
│   └── (decision_table.py, pairwise.py — coming soon)
├── parsers/         # AI layer for NLP requirement parsing (optional)
└── cli.py           # Command-line interface

License

MIT

Author

Yaniv (Yaniv2809) — AI-Powered QA Engineer