# Test Optimization Models: Cross-App vs. App-Level

## Overview: Two Models for Test Optimization

Test Optimization supports two primary models for generating test recommendations, ensuring efficiency across both single-application and complex, multi-component testing environments. The model used depends on whether your test stage is coupled to a specific application build or targets a whole environment.

### 1. App-Level Test Stages

This is the traditional model for test stages that execute against a single application build.

* **Definition:** An App-Level Test Stage is explicitly linked to a single application. App-Level stages are primarily executed within **CI pipelines** and are often the first testing step. The onboarding process is simpler as it only requires linking to a single application repository and a unique build identifier.
* **Applicability:** This model is for tests where coverage and test recommendations need to be tightly coupled to a single application's source code, such as:
  * **Unit Tests:** Testing a single application component.
  * **Component Tests:** Testing a service in isolation.
  * Any testing where the scope is limited to one application.
* **Recommendation Logic:** Test Optimization calculates recommendations by comparing the current application build against the immediately preceding build of that same application. This provides a precise view of code changes only within that single application.

### 2. Cross-App Test Stages

The Cross-App Test Stage provides a streamlined approach to testing in dynamic lab environments. It is onboarded directly onto a lab/environment and executes tests against **all applications** currently deployed in that environment.

* **Definition:** A Cross-App Test Stage is configured directly to a **lab or testing environment** and is not tied to any single application or build. They are designed for **Integration or System Test Environments** where multiple services interact. 
* **Applicability:** This model is ideal for integration, system, or end-to-end (E2E) test stages that span multiple services, where the state of the entire environment dictates the required test subset.
* **Recommendation Logic:** Test Optimization supports this concept by generating test recommendations at the **Lab/Environment level**, rather than the individual application or build level. This allows for accurate test impact analysis across complex, multi-component deployments.
* **Tracking Included Applications:** It is easy to track precisely which applications (AUTs - Applications Under Test) were considered for the recommendation calculation. This information is visible in the UI on the **Savings Breakdown page**, under the **AUT tab**. This ensures transparency in the optimization process.


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