To get started, I set up a load test in Gatling to simulate 10 users interacting with ChatGPT at once. The goal was to see how well ChatGPT could handle multiple interactions, especially when each question varied in complexity.
Creating a Data File with Varied Prompts: To make the test feel realistic, I put together a data file containing 100 different prompts. This variety added depth to the test and allowed me to see how ChatGPT responded to a wide range of questions, from simple to complex. It helped mirror the unpredictable nature of real-world interactions, bringing the test closer to actual use cases.
Adding Dynamic Elements: To increase the challenge, I included dynamic elements in each prompt. This added slight variations to each test run, making interactions feel more lifelike. By introducing this layer of unpredictability, the test reflected how users actually engage with ChatGPT, rather than just repeating a static set of questions.
Running the Load Test
Once the setup was complete, it was time to run the load test and observe how ChatGPT performed under pressure. This phase involved tracking response times, checking the quality of responses, and making any adjustments as needed.
Running the Test with Dynamic Prompts: With dynamic prompts in place, I tested ChatGPT’s ability to handle a variety of unpredictable inputs. Each prompt was unique, which pushed ChatGPT’s processing power and provided insights into how well it managed different types of user queries.
Validating Responses: For each prompt, I checked ChatGPT’s responses to ensure they were accurate and relevant. This was essential for understanding how consistently ChatGPT handled different types of questions under load. It was interesting to see how well ChatGPT maintained response quality across multiple simultaneous requests.
Measuring Response Times: I tracked response times for every interaction, which helped me spot any slowdowns or bottlenecks. This data provided a clear view of how ChatGPT’s performance held up under load and highlighted areas that might benefit from optimization.
Running the test gave me valuable insights into ChatGPT’s strengths and potential areas for improvement, highlighting the importance of testing with a mix of complex and varied scenarios.
Data-Driven Testing and Performance Analysis
This week, I focused on data driven testing and performance analysis to gain a well-rounded understanding of ChatGPT’s abilities. Here’s how these methods played out.
Data-Driven Testing: Using a data file with multiple prompts was key to running a thorough test. This approach allowed me to see how ChatGPT handled a range of scenarios, from simple to complex queries. Data-driven testing added depth to the load test and offered deeper insights into ChatGPT’s responsiveness.
Dynamic Prompt Integration: Adding dynamic elements made the test more complex and realistic, closely reflecting real-world usage. This approach gave a clearer picture of ChatGPT’s adaptability and how it managed the nuances of different user inputs.
Performance Measurement: By recording response times and validating outputs, I could easily spot any slowdowns or errors. This was crucial for evaluating ChatGPT’s reliability and response speed when handling multiple user interactions.
Tools
The main tools that helped make this week’s test possible were:
Gatling: Used for creating and running the load test simulations.
IntelliJ IDEA: My go to IDE for writing and managing Gatling scripts, making the whole process more seamless.
Each tool played a crucial role in executing the load test, from setup to running the test and analyzing the results. Happy Performance Engineering !! Keep Learning !! #LoadBalancing #PerformanceTesting #PerformanceAnalysis
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