Test Planning as Code

Designed for the Agentic Era

TestChimp takes a fundamentally different approach to test planning. Instead of treating test artifacts as database records locked inside a proprietary system, we treat them as code — plain markdown files organized in folders, version-controlled alongside your codebase.

This file-first approach is purpose-built for the agentic era, where AI coding assistants and testing agents need direct access to your test planning artifacts to work effectively.

The Philosophy: Files Over Database Records

Traditional test management tools store your user stories and test scenarios as database records, accessible only through their UI or proprietary APIs. This creates several problems:

• Siloed knowledge: Test plans live separately from your codebase
• Limited tooling: You can only interact with artifacts through the vendor's interface
• Agent-unfriendly: AI tools can't easily read or understand your test strategy
• Vendor lock-in: Migrating to another tool means complex data exports

TestChimp inverts this model. Your user stories and test scenarios are markdown files with frontmatter — human-readable, version-controllable, and instantly accessible to any tool that can read files.

Why Markdown Files with Frontmatter?

Human-Readable, Machine-Parseable

Each test artifact is a .md file with YAML frontmatter for structured metadata:

---
title: User Login with Email
priority: high
status: draft
labels:
  - authentication
  - core-flow
---

## Description

As a registered user, I want to log in with my email and password...

## Acceptance Criteria

- User can enter email and password
- Invalid credentials show clear error message
- Successful login redirects to dashboard

This format is:

• Readable by humans: Open in any text editor or IDE
• Parseable by machines: Structured frontmatter enables automated processing
• Diffable: Standard git diffs show exactly what changed
• Searchable: grep, ripgrep, or any code search tool works perfectly

Folder Organization Provides Context

Stories and scenarios are organized in a folder hierarchy that mirrors your application's structure:

stories/
├── authentication/
│   ├── login.md
│   ├── registration.md
│   └── password-reset.md
├── checkout/
│   ├── cart-management.md
│   └── payment-processing.md
└── user-profile/
    ├── settings.md
    └── preferences.md

scenarios/
├── authentication/
│   ├── login-happy-path.md
│   ├── login-invalid-credentials.md
│   └── registration-validation.md
└── checkout/
    ├── add-to-cart.md
    └── complete-purchase.md

This structure:

• Groups related artifacts: Authentication stories and scenarios live together
• Enables scoped analysis: Get insights for just the checkout module
• Provides AI context: Agents understand relationships through folder proximity
• Scales naturally: Add new folders as your application grows

Perfect for AI Coding and Testing Agents

The file-first approach unlocks powerful capabilities for AI agents:

Direct Repository Access

AI coding assistants like Cursor, GitHub Copilot, and others can directly read your test planning files because they're in your repository. When an agent is working on the login feature, it can:

1. Read stories/authentication/login.md to understand requirements
2. Browse related scenarios in scenarios/authentication/
3. Understand the broader context from sibling stories
4. Generate code that aligns with your documented expectations

Folder Context for Better Understanding

When an AI agent reads a scenario file, the folder structure provides implicit context:

• Parent folder: What feature area is this scenario in?
• Sibling files: What related scenarios exist?
• Story references: Which user stories does this scenario validate?

This contextual information helps agents make better decisions without explicit prompting.

Plain Text for Universal Compatibility

Markdown is the lingua franca of developer tools. Your test artifacts work with:

• IDEs: VSCode, IntelliJ, Cursor all render markdown beautifully
• Git platforms: GitHub, GitLab render markdown with full formatting
• Documentation tools: Docusaurus, MkDocs can include your test plans
• AI tools: Any LLM can read and understand markdown

Workflow Management Layered on Top

While the files are the source of truth, TestChimp layers enterprise-grade workflow capabilities on top:

Collaborative Editing

The TestChimp platform provides:

• Rich form-based editing: Edit with structured forms, not just raw markdown
• Real-time collaboration: Multiple team members can work simultaneously
• AI-assisted authoring: Generate stories and scenarios with context-aware AI
• Status tracking: Manage workflow states (draft, in-review, approved)

Insights at Any Granularity

Because artifacts are organized in folders, TestChimp can roll up insights at any level:

• Folder-level metrics: Coverage gaps for the entire authentication module
• Filtered views: High-priority stories due next sprint
• Cross-cutting analysis: All failing scenarios across the application

Workflow Without Lock-in

The platform enhances your workflow without trapping your data:

• Files remain readable outside TestChimp
• Export to Git keeps everything in your repository
• No proprietary formats to decode

Key Benefits Summary

Benefit	Description
Version Controllable	Sync to your Git repo so test plans live alongside your codebase, with full history and branching support
Agent-Ready	Plain markdown files with folder organization provide perfect context for AI coding and testing agents
Workflow Management	Full enterprise workflow capabilities layered on top via the TestChimp platform
Granular Insights	Coverage gaps, failing scenarios, and metrics rolled up to any folder level for actionable reporting
No Lock-in	Standard markdown files mean you're never trapped in a proprietary format
Universal Tooling	Works with any text editor, IDE, git tool, or AI assistant

Getting Started

Ready to embrace test planning as code?

1. Author User Stories — Learn how to create detailed user stories with AI assistance
2. Author Test Scenarios — Create and link test scenarios to your stories
3. Export to Git — Sync your test plan to your repository
4. Requirement Traceability — Track coverage at any folder level

Test planning as code is the natural evolution for teams working with AI-assisted development. Your test artifacts become first-class citizens of your codebase, accessible to both humans and agents.