Integration testing in Rest API - Time & Space Complexity

Time Complexity: Integration testing

O(n)

Understanding Time Complexity

When we run integration tests on REST APIs, we want to know how the time it takes grows as we add more tests or more API calls.

We ask: How does the total test time change when the number of API calls increases?

Scenario Under Consideration

Analyze the time complexity of the following integration test code snippet.

for (let endpoint of endpoints) {
  const response = await fetch(endpoint);
  console.assert(response.status === 200);
}
    

This code tests multiple API endpoints one by one, checking if each returns a successful response.

Identify Repeating Operations

Look at what repeats in the code:

• Primary operation: Sending a request to each API endpoint and checking the response.
• How many times: Once for each endpoint in the list.

How Execution Grows With Input

As the number of endpoints grows, the total test time grows roughly the same way.

Input Size (n)	Approx. Operations
10	10 API calls and checks
100	100 API calls and checks
1000	1000 API calls and checks

Pattern observation: Doubling the number of endpoints roughly doubles the total test time.

Final Time Complexity

Time Complexity: O(n)

This means the total time grows in direct proportion to the number of API endpoints tested.

Common Mistake

[X] Wrong: "Running more tests won't affect total time much because each test is fast."

[OK] Correct: Even if each test is quick, running many tests adds up, so total time grows with the number of tests.

Interview Connect

Understanding how test time grows helps you design efficient test suites and shows you think about real-world software quality.

Self-Check

"What if we ran all API calls in parallel instead of one by one? How would the time complexity change?"