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 <.

Practice

1. What does response time measure in performance testing?

easy

A. The total number of requests per second

B. How fast a single request is completed

C. The amount of data transferred in a request

D. The number of users accessing the system

Solution

1. Step 1: Understand response time meaning

   Response time measures the duration taken to complete one request from start to finish.

2. Step 2: Differentiate from throughput

   Throughput measures how many requests happen over time, not the speed of one request.

3. Final Answer:

   How fast a single request is completed -> Option B

4. Quick Check:

   Response time = speed of one request [OK]

Hint: Response time = speed of one request [OK]

Common Mistakes:

• Confusing response time with throughput
• Thinking response time counts total requests
• Mixing data size with response time

2. Which of the following is the correct unit commonly used to measure throughput?

easy

A. Milliseconds (ms)

B. Bytes

C. Requests per second (RPS)

D. Percentage (%)

Solution

1. Step 1: Identify throughput unit

   Throughput measures how many requests are processed per unit time, commonly requests per second (RPS).

2. Step 2: Exclude other units

   Milliseconds measure time, bytes measure data size, and percentage is not a throughput unit.

3. Final Answer:

   Requests per second (RPS) -> Option C

4. Quick Check:

   Throughput = requests per second [OK]

Hint: Throughput counts requests per second [OK]

Common Mistakes:

• Using time units for throughput
• Confusing data size with throughput
• Selecting percentage as throughput unit

3. Given a system that processes 120 requests in 10 seconds, what is the throughput?

medium

A. 10 requests per second

B. 0.12 requests per second

C. 1200 requests per second

D. 12 requests per second

Solution

1. Step 1: Calculate throughput formula

   Throughput = Total requests / Total time = 120 / 10 seconds.

2. Step 2: Perform division

   120 divided by 10 equals 12 requests per second.

3. Final Answer:

   12 requests per second -> Option D

4. Quick Check:

   120 ÷ 10 = 12 [OK]

Hint: Divide total requests by total time [OK]

Common Mistakes:

• Dividing time by requests instead of requests by time
• Multiplying requests and time
• Confusing throughput with response time

4. A tester recorded the following response times (in ms): 100, 200, 150, 250. The average response time was incorrectly calculated as 700 ms. What is the correct average response time?

medium

A. 175 ms

B. 700 ms

C. 150 ms

D. 200 ms

Solution

1. Step 1: Sum all response times

   Add 100 + 200 + 150 + 250 = 700 ms total.

2. Step 2: Calculate average response time

   Divide total 700 ms by 4 requests = 175 ms average.

3. Final Answer:

   175 ms -> Option A

4. Quick Check:

   700 ÷ 4 = 175 [OK]

Hint: Sum times, then divide by count [OK]

Common Mistakes:

• Using total sum as average
• Dividing by wrong number of requests
• Mixing units in calculation

5. A web server has a throughput of 50 requests per second and an average response time of 200 ms. If the server's throughput doubles but response time remains the same, what happens to the server's capacity and user experience?

hard

A. Capacity doubles; user experience stays consistent

B. Capacity stays same; user experience worsens

C. Capacity doubles; user experience worsens

D. Capacity halves; user experience improves

Solution

1. Step 1: Understand throughput doubling

   Doubling throughput means the server can handle twice as many requests per second, so capacity doubles.

2. Step 2: Analyze response time unchanged

   Since response time stays at 200 ms, the speed per request remains consistent, so user experience does not worsen.

3. Final Answer:

   Capacity doubles; user experience stays consistent -> Option A

4. Quick Check:

   Throughput ↑, response time same = better capacity, same experience [OK]

Hint: More throughput + same response time = better capacity, same experience [OK]

Common Mistakes:

• Assuming response time worsens with throughput increase
• Confusing capacity with response time
• Thinking capacity decreases when throughput increases