How to Integrate AutomationView with GitLab CI for Automated PLC Testing

The traditional approach to PLC programming—where code is tested only during factory acceptance or commissioning—is no longer viable for modern, complex systems. As industrial automation embraces software engineering practices, integrating your PLC logic into a CI/CD pipeline has become a necessity. By leveraging AutomationView’s native Git collaboration and simulation capabilities, you can build a robust automated testing pipeline. Here is how to integrate AutomationView with GitLab CI for automated PLC testing.

Key Takeaways

• AutomationView’s native Git support makes it the perfect candidate for CI/CD pipelines.
• You can trigger headless simulations in GitLab CI runners using the AutomationView CLI.
• Automated testing prevents regressions from reaching the plant floor, drastically reducing commissioning time.

flowchart LR
    A["Developer Push"]:::blue --> B["GitLab CI Runner"]:::green
    B --> C["AutomationView CLI"]:::blue
    C --> D["Headless Simulation"]:::green
    D --> E["Test Report"]:::red

    classDef blue fill:#2563eb,stroke:#1e3a8a,stroke-width:2px,color:#ffffff
    classDef green fill:#16a34a,stroke:#14532d,stroke-width:2px,color:#ffffff
    classDef red fill:#dc2626,stroke:#7f1d1d,stroke-width:2px,color:#ffffff

Why Shift Left in PLC Engineering?

In standard PLC environments, logic errors are often discovered during physical commissioning, leading to costly downtime and project delays. By “shifting left”—testing early and often—you catch bugs when they are cheapest to fix. AutomationView GitLab CI integration allows teams to run unit tests on SFC/Grafcet sequences every time a developer pushes a commit. This is crucial for multi-developer teams working on large-scale material handling or process control systems, where merge conflicts or unintended side effects are common.

Setting Up the GitLab CI Runner

To run AutomationView tests in the cloud or on a local server, you need a GitLab Runner configured with the AutomationView CLI. The CLI allows you to execute Python-based unit tests and sequence simulations headlessly without the GUI.

flowchart TD
    subgraph ci_pipeline ["GitLab CI Pipeline"]
        step1["Linting & Syntax Check"]:::blue --> step2["Unit Tests (SFC Logic)"]:::green
        step2 --> step3["Integration Simulation"]:::green
        step3 --> step4["Deploy to Staging PLC"]:::red
    end

    classDef blue fill:#2563eb,stroke:#1e3a8a,stroke-width:2px,color:#ffffff
    classDef green fill:#16a34a,stroke:#14532d,stroke-width:2px,color:#ffffff
    classDef red fill:#dc2626,stroke:#7f1d1d,stroke-width:2px,color:#ffffff

1. Defining the .gitlab-ci.yml

Create a .gitlab-ci.yml file at the root of your AutomationView project repository. This file will define the stages of your pipeline. A basic pipeline consists of a test stage that invokes the AutomationView test runner.

2. The Headless Test Execution

When the pipeline triggers, the runner pulls the latest commit and uses the AutomationView CLI to run your pre-defined Python test scripts against the SFC logic. The CLI outputs standard JUnit XML reports, which GitLab automatically parses to display pass/fail metrics directly in the merge request interface.

Handling Simulation State in the Pipeline

One of the main challenges engineers face is simulating external physical I/O during an automated test. Without physical sensors, the sequence will hang waiting for transitions.

For a GitLab CI pipeline, the Python Mock Hooks approach is generally the best. AutomationView’s Python backend allows you to inject simulated states (e.g., “Sensor_A = True” after 2 seconds) directly into the test context, ensuring the SFC transitions proceed logically.

Reviewing Test Artifacts

Once the pipeline completes, the results are vital. If a sequence fails due to an unexpected state, the AutomationView CLI can export the trace log. You can configure GitLab CI to save this trace as an artifact, which developers can download and replay locally in the AutomationView visualizer to pinpoint the exact step where the logic deviated.

Conclusion

Integrating AutomationView GitLab CI pipelines brings modern software reliability to industrial automation. By automating your PLC testing, you protect the master branch from regressions and ensure that your team deploys sequences with absolute confidence. Start shifting left today and experience the difference during your next commissioning phase.

Ready to upgrade your engineering workflow? Explore the AutomationView Store for tools and templates that accelerate your projects.

FAQ

Do I need a dedicated server for the GitLab Runner?

Yes, for AutomationView CLI executions, it is highly recommended to host a dedicated runner (either a local VM or a cloud instance) that has the appropriate AutomationView runtime and licensing installed.

Can I test safety logic in the CI pipeline?

While standard logic can be simulated, safety logic should still undergo rigorous formal verification and physical validation, as standard CI simulations cannot guarantee deterministic timing or hardware-level fail-safes.

Does this replace physical commissioning?

No. Automated testing drastically reduces logic and sequence errors, saving significant time, but it does not replace the need for physical I/O checks, tuning, and real-world commissioning.