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?"

Practice

1. What is the main goal of integration testing in REST API development?

easy

A. To measure the speed of the server hardware

B. To test the user interface design

C. To check if different parts of the application work together correctly

D. To write documentation for the API

Solution

1. Step 1: Understand integration testing purpose

   Integration testing focuses on verifying that multiple components or services interact correctly.

2. Step 2: Compare options with integration testing goal

   Only To check if different parts of the application work together correctly describes checking the cooperation of different parts, which matches integration testing.

3. Final Answer:

   To check if different parts of the application work together correctly -> Option C

4. Quick Check:

   Integration testing = check parts work together [OK]

Hint: Integration tests check connections between parts, not UI or speed [OK]

Common Mistakes:

• Confusing integration testing with UI testing
• Thinking integration tests measure performance
• Believing integration tests are for documentation

2. Which of the following is the correct way to send a GET request in a Python integration test using the requests library?

easy

A. response = requests.get('http://api.example.com/data')

B. response = requests.post('http://api.example.com/data')

C. response = requests.fetch('http://api.example.com/data')

D. response = requests.send('http://api.example.com/data')

Solution

1. Step 1: Recall correct method for GET request in requests library

   The method to send a GET request is requests.get().

2. Step 2: Check options for correct syntax

   Only response = requests.get('http://api.example.com/data') uses requests.get() correctly with a URL string.

3. Final Answer:

   response = requests.get('http://api.example.com/data') -> Option A

4. Quick Check:

   GET request method = requests.get() [OK]

Hint: Use requests.get() for GET requests, not post, fetch, or send [OK]

Common Mistakes:

• Using requests.post() instead of get() for GET requests
• Using non-existent methods like fetch() or send()
• Missing quotes around the URL string

3. Consider this Python integration test snippet using requests:

response = requests.post('http://api.example.com/login', json={'user':'admin', 'pass':'1234'})
print(response.status_code)

What will be the printed output if the login is successful and the server returns HTTP 200?

medium

A. 200

B. 403

C. 500

D. 404

Solution

1. Step 1: Understand HTTP status codes

   HTTP 200 means success, 404 means not found, 500 means server error, 403 means forbidden.

2. Step 2: Analyze the code output

   The code prints response.status_code. If login is successful, server returns 200, so output is 200.

3. Final Answer:

   200 -> Option A

4. Quick Check:

   Success status code = 200 [OK]

Hint: Successful HTTP response status is 200 [OK]

Common Mistakes:

• Confusing 404 (not found) with success
• Assuming 500 means success
• Mixing 403 (forbidden) with success

4. You wrote this integration test code:

response = requests.get('http://api.example.com/items')
assert response.status_code == 200
assert response.json()['items']

But the test fails with a KeyError on 'items'. What is the likely cause?

medium

A. The URL is incorrect and returns 404

B. The requests library is not imported

C. The status code is not 200

D. The response JSON does not contain the key 'items'

Solution

1. Step 1: Understand KeyError cause

   A KeyError means the key 'items' is missing in the JSON response.

2. Step 2: Analyze test failure reason

   Since status code is asserted as 200, the URL is likely correct and requests imported. The error is missing 'items' key in JSON.

3. Final Answer:

   The response JSON does not contain the key 'items' -> Option D

4. Quick Check:

   KeyError means missing key in JSON [OK]

Hint: KeyError means JSON key missing, not URL or import error [OK]

Common Mistakes:

• Assuming URL is wrong without checking status code
• Ignoring that status code 200 means request succeeded
• Forgetting to import requests library

5. You want to write an integration test that verifies your REST API endpoint /users returns only active users. The API returns JSON like {"users": [{"id":1,"active":true}, {"id":2,"active":false}]}. Which test code snippet correctly checks this condition?

hard

A. response = requests.get('http://api.example.com/users')
users = response.json()['users']
assert any(user['active'] for user in users)

B. response = requests.get('http://api.example.com/users')
users = response.json()['users']
assert all(user['active'] for user in users)

C. response = requests.get('http://api.example.com/users')
users = response.json()['users']
assert user['active'] == True

D. response = requests.get('http://api.example.com/users')
assert response.status_code == 404

Solution

1. Step 1: Understand requirement to check all users are active

   The test must confirm every user in the list has active set to true.

2. Step 2: Analyze code snippets for correctness

   response = requests.get('http://api.example.com/users') users = response.json()['users'] assert all(user['active'] for user in users) uses all() to check all users are active, which matches requirement. response = requests.get('http://api.example.com/users') users = response.json()['users'] assert any(user['active'] for user in users) checks if any user is active (not enough). response = requests.get('http://api.example.com/users') users = response.json()['users'] assert user['active'] == True references undefined variable user outside loop. response = requests.get('http://api.example.com/users') assert response.status_code == 404 checks for 404 status, which is wrong.

3. Final Answer:

   response = requests.get('http://api.example.com/users') users = response.json()['users'] assert all(user['active'] for user in users) -> Option B

4. Quick Check:

   Use all() to verify all users active [OK]

Hint: Use all() to confirm every user is active [OK]

Common Mistakes:

• Using any() instead of all() to check all users
• Referencing undefined variables in assertions
• Checking wrong HTTP status code