Part 1: Scaling Test Coverage with Data-Driven Testing – The Apptest.ai Approach

📊 Why Data-Driven Testing?

When implementing mobile app testing, it’s common to encounter scenarios where the workflow remains consistent, but the input values vary.
Login, search, checkout — these features follow the same flow but require testing with a wide range of data combinations.

Creating separate test cases or duplicating flows manually introduces inefficiencies and additional maintenance overhead for your test automation process.
A more scalable and structured approach is Data-Driven Testing.

💡 What Is Data-Driven Testing?

Data-driven testing allows you to define your test flow once, then run it repeatedly using external input values — such as rows from a CSV file.
This AI test automation approach enhances test coverage while reducing test maintenance overhead.

• Define the test logic once
• Use structured datasets to vary inputs
• Reuse the same test flow across multiple data variations — without duplicating scripts

Apptest.ai’s data-driven testing solution is set to launch in June 2025. Here’s an early look.

🚀 How It Works with Apptest.ai

Apptest.ai streamlines app test automation with a no-code, visual scenario builder that anyone can use.
Setup is simple — and execution is powerful, scalable, and easy to track.

Key features include:

• Create the test scenario once using Stego for scalable test automation.
• Upload a CSV file to provide your input values
• Inputs are automatically mapped to the appropriate fields in your test (called User Variables)
• Each row in the dataset becomes a separate test run with isolated results

✔️ Automate dozens of test cases from a single scenario and dataset — without writing a line of code

How It Works

1. Build your scenario in Stego

   • Create a reusable flow — login, search, checkout, etc.
   • Mark dynamic fields (e.g., email, password) as User Variables

2. Select your Test Execution Mode in Ptero

   • Parallel: run tests simultaneously
   • Sequential: run one by one
   • Combined Sequential: merge all into a single result

3. Prepare a CSV Dataset

   Example CSV:

   Name,Email,Phone John Doe,john@example.com,123-456-7890 Jane Smith,jane@example.com,234-567-8901 Mike Lee,mike@example.com,345-678-9012

   1 2 3 4 5

   Name,Email,Phone John Doe,john@example.com,123-456-7890 Jane Smith,jane@example.com,234-567-8901 Mike Lee,mike@example.com,345-678-9012

4. Execute and Review

   • Each row triggers its own test
   • Results are matched with inputs in the Test Runs dashboard for easy failure diagnosis

🔧 Practical Use Cases

• Login validation: Test multiple user credentials for mobile app testing
• Form validation: Try combinations of name, email, and phone inputs
• Search testing: Validate behavior for various keywords
• Checkout flow: Test different payments, discounts, and amounts

🌟 Key Benefits

• Improved test efficiency: Execute large-scale, repeated tests quickly using extensive datasets
• Expanded coverage: Test both structured and exploratory app behaviors through AI test automation
• Simplified maintenance: Keep scenarios clean by separating test data from test logic for mobile QA process optimization

Data-driven testing goes beyond repetitive app test automation.

It gives teams a scalable, structured strategy for achieving test consistency, broader coverage, and lower maintenance costs.

Apptest.ai makes this easy with a no-code approach that empowers QA engineers, product managers, and non-technical team members alike.

💡 Flexible Inputs. Streamlined Flows.

Launching June 2025 — Apptest.ai’s data-driven testing transforms repetitive test flows into a scalable, structured QA strategy for mobile apps.