unclebob

unclebob / Acceptance-Pipeline-Specification

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Portable acceptance pipeline specification

43
1
100% credibility
Found May 14, 2026 at 43 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
AI Summary

This repository defines a standard way to transform plain-language feature descriptions into automated software tests and validate their effectiveness through targeted reviews.

How It Works

1
🔍 Discover the Guide

You hear about a smart way to make sure software matches simple everyday descriptions of what it should do.

2
📖 Explore the Instructions

You read the clear guide that shows how to turn plain language into reliable checks for any project.

3
✏️ Write Your Description

You describe what your software needs to do using easy sentences like setup, action, and expected result.

4
🛠️ Prepare Your Project

You follow the friendly steps to set up the system that reads your description and creates checks.

5
▶️ Run the First Check

With one go, it runs the checks and tells you if your software behaves exactly as described.

6
🔍 Test Check Strength

You run a deeper review that imagines tiny changes to spot if your checks are truly thorough.

🎉 Feel Total Confidence

Your software now has strong, proven checks that catch issues and match your vision perfectly.

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AI-Generated Review

What is Acceptance-Pipeline-Specification?

This repo from Uncle Bob lays out a portable specification for an acceptance testing pipeline that any project can adopt, regardless of language. It takes simple Gherkin feature files, parses them to a JSON intermediate format, generates executable tests, runs them via your project's test runner, and runs mutation tests on example data to check if your acceptance tests are truly connected to the code. Developers get CLI entry points like gherkin-parser, acceptance-generator, and gherkin-mutator for normal runs and mutation analysis, making it a drop-in for portable acceptance tests in setups like portable Python or portable Windows environments.

Why is it gaining traction?

Unlike language-tied BDD tools, this spec stays neutral with a fixed JSON IR and deterministic mutations on examples—booleans flip, numbers tweak, strings dither—revealing weak acceptance tests that pass despite spec changes. The mutation reports (text or JSON) pinpoint survivors, pushing devs to tighten examples without project-specific hacks. It's a hook for teams eyeing portable GitHub CLI or desktop workflows, emphasizing test quality over volume.

Who should use this?

QA engineers or backend teams writing Gherkin specs for APIs who want mutation testing to validate example robustness. Polyglot shops with mixed Python, Java, or Node projects needing a shared portable acceptance pipeline. Devs maintaining portable apps like GitHub portable download tools or Steam launchers, where cross-platform test stability matters.

Verdict

Adopt if you're building robust acceptance tests and can implement the spec—43 stars and 1.0% credibility reflect its early, spec-only stage with excellent docs but no reference code. Solid foundation from a clean code legend, worth prototyping for mutation-driven quality gains.

(198 words)

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