farley-properties-and-scoring

You can evaluate your automated test suite against Farley's 8 properties by running a two-phase analysis combining static Python scripts for deterministic calculations and LLM judgment for semantic evaluation, producing a Farley Index score.

Dave Farley's 8 properties framework assesses test quality across Understandable, Maintainable, Repeatable, Atomic, Necessary, Granular, Fast, and First properties to ensure tests contribute maximum value to the development process.

You score test code quality by applying detailed scoring rubrics and weighted formulas to assess maintainability, understandability, and TDD evidence, yielding an objective Farley Index score for your tests.

Test suite analysis requires Python scripts, specifically cli_calculator.py, to perform deterministic calculations during the static analysis phase before the LLM conducts semantic evaluation of the test code.

The best way to audit tests for TDD evidence and granularity is to use a structured, data-driven methodology that applies objective scoring rubrics to evaluate whether tests meet Farley's necessary and granular property criteria.

You cannot rely solely on LLM judgment because the Skill utilizes a two-phase process where static analysis calculates deterministic metrics first, ensuring objective evaluation before semantic LLM assessment of test code.