Performance metrics (response time, throughput) in Testing Fundamentals - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to calculate the average response time from a list of response times.

Testing Fundamentals

average_response_time = sum(response_times) / [1]

Drag options to blanks, or click blank then click option'

Aresponse_times.count()

Blen(response_times)

Cmin(response_times)

Dmax(response_times)

Attempts:

3 left

💡 Hint

Common Mistakes

Using max() or min() instead of len() to count items.

Trying to call count() without arguments on the list.

The average response time is calculated by dividing the total sum of response times by the number of response times, which is given by len(response_times).

2fill in blank

medium

Complete the code to calculate throughput given total requests and total time in seconds.

Testing Fundamentals

throughput = total_requests / [1]

Drag options to blanks, or click blank then click option'

Atime_per_request

Btotal_requests

Cresponse_time

Dtotal_time

Attempts:

3 left

💡 Hint

Common Mistakes

Dividing by total_requests instead of total_time.

Using response_time instead of total_time.

Throughput is the number of requests processed per unit time, so we divide total requests by total time.

3fill in blank

hard

Fix the error in the code to correctly calculate average response time from a list of response times.

Testing Fundamentals

average_response_time = sum(response_times) / [1]

Drag options to blanks, or click blank then click option'

Alen(response_times)

Bresponse_times.length

Cresponse_times.count()

Dcount(response_times)

Attempts:

3 left

💡 Hint

Common Mistakes

Using response_times.length which is not valid in Python.

Trying to call count() without arguments.

In Python, the correct way to get the number of items in a list is len(response_times). Using response_times.length or count() is incorrect here.

4fill in blank

hard

Fill both blanks to create a dictionary that maps each request ID to its response time, but only include requests with response time less than 500 ms.

Testing Fundamentals

fast_responses = {req_id: [1] for req_id, [2] in responses.items() if response_time < 500}

Drag options to blanks, or click blank then click option'

Aresponse_time

Breq_time

Dtime

Attempts:

3 left

💡 Hint

Common Mistakes

Using different variable names inconsistently.

Not unpacking the dictionary items correctly.

The dictionary comprehension maps each req_id to its response_time. The loop unpacks responses.items() into req_id and response_time. The filter keeps only those with response time less than 500 ms.

5fill in blank

hard

Fill all three blanks to create a dictionary that maps each user to their average response time, including only users with average response time less than 300 ms.

Testing Fundamentals

average_times = {user: sum(times) / [1] for user, times in user_responses.items() if (sum(times) / [2]) [3] 300}

Drag options to blanks, or click blank then click option'

Alen(times)

C<

D>

Attempts:

3 left

💡 Hint

Common Mistakes

Using '>' instead of '<' in the filter condition.

Using different values for length in numerator and denominator.

To calculate average response time, divide the sum of times by the number of times using len(times). The filter keeps users whose average response time is less than 300 ms, so the comparison operator is <.