Artificial Intelligence

AI-Driven Testing in action

How to manage regression testing with better, smarter and more robust test automation. Try Agilitest for free. Open Source scripting format.

Scalable Data-Driven Testing with Functional ATS

Functional decomposition + decoupled data = scalable automation This demo shows how Agilitest enables true data-driven testing by combining: • Functional decomposition (a reusable subscript that fills the form) • Full separation between logic and data (external CSV file) • Deterministic ATS execution without AI dependency ‍ The Objective Instead of generating one large automation script, we design: ✅ A functional subscript responsible only for filling the form ✅ A parent script that iterates over a dataset ✅ A separate CSV file containing multiple test profiles This creates: • Reusability • Scalability • Clean architecture • Maintainability • And most importantly: The final automation is pure ATS — no AI required to execute it. ‍ The AI generates the script and functional tree The AI proposes a complete Data File Set ATS guarantees its execution

Step 1 — Defining the Data-Driven Intent

Claude receives instructions to create a data-driven test scenario with multiple profiles. ‍ The objective is to validate several combinations of: • Roles • Pass types • Themes • Experience levels • Budget slider • Optional fields Instead of duplicating logic, we will factorize behavior.

Step 2 — Identifying Form Elements

Using the REPL, Claude identifies all interactive components: • Text inputs • Select fields • Checkboxes • Radio buttons • Slider • Toggles • Submit button This ensures we build a reliable functional subscript.

Step 3 — First Functional Entry

A first profile is entered interactively to validate: • Correct selectors • Expected behavior • Budget slider value control This validates the functional flow before generalizing it.

Step 4 — Graphical Slider vs Real Value

Although the slider is graphical, ATS retrieves the real numeric value for assertion. This guarantees: • Visual interaction • Functional verification • Deterministic validation We validate actual business values — not pixels.

Step 5 — Second Profile Validation

A second profile is tested to confirm: • Different role • Different pass • Different budget • Optional fields left empty This ensures the flow supports variable data.

Step 6 — Creating the Functional Subscript and Data File

Claude generates: A reusable subscript: remplirInscription.ats ‍ A CSV file: inscriptions.csv ‍ The subscript contains only the functional logic to fill the form. The CSV contains only data. This is the core principle: Logic ≠ Data

Step 7 — Parent Script Orchestration

The parent script: • Opens the browser • Calls the subscript • Passes each row of the CSV • Closes the channel This creates a clean layered architecture: Parent script → Functional subscript → Data file

Step 8 — Generated Data File

The CSV contains multiple varied profiles: • Students • Developers • Senior profiles • Different budgets • Optional fields populated or empty The test coverage is driven by data variation — not script duplication.

Step 9 — Conditional Logic in the Subscript

The subscript includes conditions such as: IfNotEmpty($param(phone)) → enter value This ensures: • Optional fields are handled properly • Empty CSV values do not break the flow • The script remains generic and reusable

Step 10 — Compiled ATS Execution

The REPL is closed. The test is compiled and executed in standard ATS mode. At this stage: • No AI is required • No Claude runtime is involved • The script runs in CI/CD normally The result is a pure functional ATS data-driven test.

What This Demonstrates

This demo demonstrates three critical principles: 1. Functional Decomposition A reusable subscript encapsulates business behavior: Fill the form Handle optional values Validate the budget 2. Data Decoupling The CSV is fully independent: Test scenarios evolve without touching the logic New cases are added by editing data only Non-technical users can modify test coverage 3. Deterministic Execution The final artifact is: Human-readable ATS Versionable in Git Executable in CI/CD Independent from AI AI accelerates the creation phase. ATS guarantees execution stability.

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