Docs / Concepts / Declarative Methods

Understanding Declarative Methods in LPS

The declarative methods provide a way to express dynamic values inside test scenarios without writing code. They allow test authors to describe what value is needed rather than how to compute it. This makes scenarios concise, repeatable, and easy to read.

What Are Declarative Methods?

Declarative methods are inline expressions prefixed with $ (e.g., $random, $guid, $timestamp). When LPS encounters them in a test definition, it evaluates the method and replaces it with the resulting value.

url: "https://api.example.com/users?id=$guid()"

Here, $guid() is declarative: the test definition simply states "use a GUID here". The tool decides how to generate it.

Why Declarative?

  • Simplicity – No need to embed scripts or write imperative logic.
  • Reusability – Values can be stored in variables and referenced later.
  • Consistency – Same syntax across URLs, headers, bodies, and variable initializers.
  • Focus – Test authors can concentrate on scenario design, not implementation details.

Declarative vs Imperative

Imperative Approach

Write custom code (e.g., in JavaScript or Python) to generate values, manage state, and inject them into requests.

Declarative Approach (LPS)

Use predefined methods like $random, $timestamp, $jwtclaim. The "how" is abstracted away; you only declare the intention.

Key Benefits in LPS

1. Dynamic Data Generation

Methods like $random, $generateemail, $counter ensure every request can have unique, realistic values.

2. State Handling Without Code

Counters ($counter) and variable storage let you maintain sequence or correlation across multiple requests.

3. Interoperability

Methods can be embedded anywhere — URLs, headers, request bodies, or variables — making scenarios flexible.

4. Testing at Scale

Declarative expressions are lightweight and evaluated at runtime, enabling high-volume load testing without scripting overhead.

When to Use Them

  • Whenever you need dynamic values (IDs, timestamps, tokens).
  • To correlate values between steps (store with variable=... and reuse).
  • To avoid scripting while still achieving variability.
  • As a bridge between static test definitions and runtime behavior.

Conclusion

Declarative methods are a cornerstone of LPS. They turn static YAML configurations into dynamic, data-driven test scenarios, empowering testers to model real-world interactions at scale without writing imperative code.

By understanding the concept — declarative means "describe what you want, not how to do it" — you can design scenarios that are both powerful and maintainable.