ChatGPT Use Cases For Performance Engineers

This post will explain about ChatGPT use cases for performance engineers. Internet traffic is being flooded by ChatGPT. Without even one mention of ChatGPT on my socials, my feed would be incomplete. If I don’t write about ChatGPT, it’s a sin. Since the beta release was announced, I have been using ChatGPT.

ChatGPT Use Cases For Performance Engineers

In this article, you can know about ChatGPT Use Cases For Performance Engineers here are the details below;

I’ve been experimenting with the console and offering comments on its responses. It is enjoyable and aids in learning at the same time. Learn about ChatGPT in this blog post, as well as how to use it to address common issues faced by performance engineers.

What Is ChatGPT?

Let’s first gain a fundamental understanding of GPT. Generative Pre-trained Transformer is known as GPT. It’s a lot to take in. Let’s put it plainly. It is a particular kind of AI model that has been taught to produce text that resembles that of a human. Also check free online video chat websites.

The model learns the linguistic patterns and structures through training on a vast sample of text, including books, papers, and websites. It can be used to produce new text that is similar to the text it was trained on once it has been taught. It can be used to complete a range of tasks, including text summary, question answering, and language translation.

• The words that are crucial have been bolded.
• In essence, you feed a massive amount of data into an AI model to train it.
• In order for it to provide new information based on what it has been educated for.
• Is it like a search engine, you might ask?
• NO, is the answer.
• Based on a question you type, a search engine searches the internet and displays the results.
• But using the training it received from the dataset, GPT creates new text.
• It is unable to access the internet (at this moment).

ChatGPT SignUp

It is an easy and uncomplicated procedure.

Use Cases for Performance Engineers

Advertising, writing, screenplays, poems, website status updates, development, testing, and more are just a few of the many possible use cases for ChatGPT.

• Screenplay for ChatGPT Status: ChatGPT Use Cases for Performance Engineers
• Screenplay of ChatGPT Status
• The important ChatGPT application cases for performance engineers will be shown.
• You can ask ChatGPT to explain performance testing to your child, your parents, your manager, the CTO, the CEO, the prime minister, and others.
• For your application, request that ChatGPT create a performance test strategy.
• Request a feature comparison from ChatGPT.
• Request that ChatGPT examine the results of the performance test.
• For performance testing, ask ChatGPT to develop code.
• Request a list of the dangers associated with performance testing from ChatGPT.
• Request that ChatGPT create a tool for performance testing.
• Even Einstein had inquiries.
• Now, let’s ask ChatGPT.

Explain performance testing to a 6-year-old kid

“Explain performance testing to a 6-year-old child,” I demanded.

The ChatGPT’s reply is provided below.

It made a connection between performance testing and a toy as an example.

Good, huh?

Similar to how you test a toy before using it, performance testing.

It ensures that the toy functions properly and is durable.

For instance, if you were testing a toy car, you may be interested in its top speed, turning ability, and battery life.

In a similar vein, performance testing is carried out on websites and computer programmes to ensure that they operate properly and don’t crash or load slowly.

It’s similar to giving a computer software a checkup to make sure it’s healthy and functions properly.

Let’s switch gears and make a challenging request.

Write a performance test plan for your application

For your application, I requested that ChatGPT create a performance test strategy.

We shouldn’t answer it verbatim.

Information needs to be fed as shown below.

For the following requirements, create a performance test plan for my application:

– Begin with performance testing objectives.

– Web-based banking application

5000 peak users are anticipated during regular business hours. Response times are anticipated to be between one and three seconds. Also check  team chat for remote businesses.

– Test scenarios include creating an account, logging in, adding a payee, transferring funds, checking the amount, applying for a loan, editing your profile, and logging out.

Use any open-source or paid performance testing tool with a Java, Python, or Go base.

– Chronology:

Test writing takes two weeks, test execution takes three weeks, and results analysis and signoff take one week. Requirements collection and test environment validation take one week.

– Load, stress, and endurance testing types

– Add a section for the lifespan of defects.

– Include a section listing known problems

– Include a component for managing test data – Include a part for references

The ChatGPT’s reply is provided below.

As you can see, ChatGPT provided the necessary context to the information from the aforementioned query, such as test data management and problems lifecycle.

It is a high level, and I agree.

This can’t be used exactly as is in your project.

I advise you to construct this according to your needs.

Performance Test Plan:

Objectives:

The banking online application’s performance and scalability under various load and stress circumstances will be evaluated. To confirm that the application can support 5000 peak users during regular business hours. To ensure that the programme adheres to the desired response time of 1-3 seconds and CPU and Memory consumption of 60%

To locate and fix any potential application performance issues Scope: Load, stress, and endurance testing techniques will be used to evaluate the banking online application.

The following situations will be put to the test: opening an account, logging in, adding a payee, transferring funds, checking the balance, applying for a loan, editing your profile, and logging out.

Any open-source or for-profit performance testing tool built on Java, Python, or Go will be used for the testing.

Timeline:

Gathering requirements and confirming the test environment: one week

2 weeks for test scripts

3-week test execution

Results evaluation and approval: one week

Types of Testing:

• Testing the application’s performance at both normal and peak loads is known as load testing.
• Testing the application’s performance under unusual and high loads is known as stress testing.
• Testing the application’s performance over an extended period of time is known as endurance testing.
• Defects Lifecycle: Using a defect tracking tool, all discovered defects will be reported and documented.
• Based on how they affect the application’s performance, flaws will be given priority.
• The task of correcting the errors will fall to the development staff.
• The task of validating the fixes will go to the test team.

Known Issues: The application’s performance may be impacted by any known issues, which will be documented and shared with the appropriate parties.

The performance of the application will be reduced as a result of these known issues.

Test Data Management: To prevent it from affecting Production Data, Test Data will be developed and handled in a distinct environment. Also check Bluestacks Snapchat not working.

Test data will be produced in a manner that corresponds to the application’s actual data and usage patterns.

Performance Testing Best Practices by ABC Performance Testing Tools Comparison by DEF Performance Testing Methodology by XYZ Performance Testing Methodology by XYZ

You may use any real references you have used or plan to use; the referenced books or articles are only hypothetical.

Compare tools’ features

I frequently get inquiries regarding particular tools.

Well, a lot of things affect the decision, including your requirements.

To compare the features of the tools, let’s ask ChatGPT.

My question:

Compare the features of JMeter, LoadRunner, k6, Locust, and NeoLoad. I don’t have any money set up to buy a tool. However, my engineers are quite good at developing code in Java, Go, or Python. For performance testing, please recommend a tool. This is another ChatGPT Use Cases For Performance Engineers.

My programme is web-based. I need to quickly inject load from several machines.

ChatGPT’s response:

It didn’t provide any conclusive advice on choosing an open-source tool. It is behaving well. But it also incorporated the features of the tools, as seen below. Popular performance testing tools for web-based applications include JMeter, LoadRunner, k6, Locust, and NeoLoad.

Here are some features to compare:

• JMeter is a Java-based open-source application that can be used for both load testing and functional testing.
• It is capable of running on numerous machines and provides distributed testing.
• It enables simple building of customised scripts and comes with a wealth of samplers and plugins.
• Additionally, it has strong reporting abilities.
• LoadRunner: A popular commercial tool for load testing is called LoadRunner.
• It offers a user-friendly interface and supports a variety of protocols.
• Additionally, it contains sophisticated capabilities including automatic correlation and real-time monitoring.
• However, the price is high.
• k6: It is a Go-based, open-source programme with a load testing emphasis.
• A performance measurement engine is embedded right into it, and the script syntax is straightforward.
• It is capable of running on numerous machines and provides distributed testing.
• Additionally, it includes a cloud service for scalability testing.
• Locust is an open-source programme with a load testing focus that was created in Python.
• It makes use of a straightforward script syntax that makes it simple to describe user behaviour.
• It is capable of running on numerous machines and provides distributed testing.
• NeoLoad: For load and performance testing, utilise NeoLoad, a commercial tool.
• It offers a user-friendly interface and supports a variety of protocols.
• Additionally, it contains sophisticated capabilities including automatic correlation and real-time monitoring.
• I would advise adopting an open-source tool like JMeter, k6 or Locust because your team excels at creating Java, Go, or Python code and you don’t have the funds to invest in a solution.

Analyze the performance testing results

Additionally, they offer vibrant communities and a multitude of online resources.

If you need to create and manage test scripts specifically, you can choose whichever of these three you are most comfortable with.

Write ode for performance testing

Let’s ask ChatGPT to examine the JMeter raw performance test findings.

NOTHING CONFIDENTIAL SHOULD BE SENT IN THE CHAT.

In order to test my hypothesis, I’m utilising dummy data.

Analyze the performance test results listed below.

I then pasted the sample data shown below into the chat.

Response from ChatGPT

The results of the performance test reveal a number of HTTP requests with a mix of 200 and 503 status codes made to the URL http://example.com.

Each request takes between 24 and 95 milliseconds to complete.

For all requests, the response message is “OK”.

The requests were made from two different thread groups, “Thread Group 1-1” and “Thread Group 1-2,” according to the thread name. This is another ChatGPT Use Cases For Performance Engineers.

All requests were successful, as shown by the success column, and no failure messages were noted.

For each request, 109 bytes were sent, and 1591 bytes were received, according to the bytes and transmitted bytes columns.

There were 2 threads in each group and 2 threads overall, as shown by the grpThreads and allThreads columns.

The columns for delay, idle time, and connect indicate that there was no idle time and that the connect time ranged from 0 to 56 milliseconds for each request.

The programme functioned well overall, with quick response times and no failures, according to these data.

The fact that there are numerous 503 status codes, which denote that the server is delivering a service unavailable error, should be noted.

It is also necessary to maintain in mind that this data is merely a sample, and more testing will be necessary to have a better idea of the application’s performance.

It accurately extracted thread counts, HTTP status codes, bytes, elapsed time, and other data.

Each parameter’s analysis was then added, and the piece ended with a general recommendation.

For performance testing, write code.

To test the performance of several tools, let’s ask ChatGPT to produce some code.

To test https://api.example.com for the Web HTTP/HTML protocol, create a LoadRunner script.

Here is an illustration of a LoadRunner script that can be used to test the Web HTTP/HTML endpoint at https://api.example.com:

Declare the web custom request function web custom request(“api example”, “define the request method and endpoint “URL=https://api.example.com”, “Method=GET,” “define any headers that need to be sent with the request “Header=Content-Type:application/json,” “Header=Accept:application/json,” “define any body data that need to be sent with the request “B

The request specifies that the request and response should be in JSON format by including “Content-Type” and “Accept” headers set to “application/json”.

To process the response correctly, the ResponseFormat is set to “JSON”.

The response information is saved using the Snapshot=t1.inf command to a file called “t1.inf” in the script’s results directory. This is another ChatGPT Use Cases For Performance Engineers.

This script is a simple illustration that can be altered to fit the test circumstance.

For instance, you can modify the script to include parameterization for various inputs, correlation and validation criteria, think time, looping, and conditions.

You must also include Vusers, Ramp up and Ramp down duration, and build the test scenario using the controller in order to load test this.

My question k6:

Create a K6 script to test https://api.example.com with a 2 second sleep in between queries and submit the findings to the K6 cloud.

ChatGPT’s response:

An illustration of a k6 script that can be used to test the https://api.example.com endpoint with a 2-second sleep in between queries and upload the outcomes to the k6 cloud is shown below:

Import sleep from “k6,” import http from “k6/http,” import trend from “k6/metrics,” etc.

Mytrend should equal new Trend(“my trend”);

Export let options = stages: [ duration: “5s”, target: 1, duration: “10s”, target: 10, duration: “5s”, target: 0, ]

thresholds: “http req duration”: [“p(95)200”], “ext: “loadimpact: name: “my test”, “, “, “;

let response = http.get(“https://api.example.com”); myTrend.add(response.timings.duration); sleep(2); export default function();

This script sends a GET request to the https://api.example.com endpoint using the http.get() method from the k6/http module.

The sleep(2) method is used to delay responses to requests by two seconds.

The length of time it takes for requests to be answered is tracked by the trend measure “my trend.”

The load test scenario is defined by the stages option, where 1 VU is used for 5 seconds, then 10 VU is used for 10 seconds, and lastly 0 VU is used for 5 seconds. This is another ChatGPT Use Cases For Performance Engineers.

The acceptable response time is determined using the thresholds option in this example, which is set to the 95th percentile of the reaction time being less than 200ms.

The test name is configured and the results are uploaded to the K6 cloud using the ext option.

As you perform the test, you must enter your cloud API key in the command line as follows:

k6 run -e script.js API KEY=YOUR API KEY

According to your test scenario, you may also add more options to this script, such as more requests, passing parameters, validation rules, and assertion rules.

My question on JMeter:

To test https://api.example.com, create a JMeter script, add a 2-second sleep between queries, then submit the test results to the Grafana cloud.

ChatGPT response:

An illustration of a JMeter script that can be used to test the https://api.example.com endpoint with a 2-second sleep in between queries and submit the findings to Grafana cloud is shown here:

1. Begin by including these plugins in your JMeter instance:

The Grafana Cloud backend listener for sending metrics.

Use the JMeter Plugins Manager to add more plugins.

Make a Thread Group and include a sampler for an HTTP Request.

2. Enter https://api.example.com as the server name or IP address.

Set GET as the Method.

3. Include a Constant Timer and set the Thread Delay to 2000 milliseconds in the HTTP Request sampler.

The additional delay will be 2 seconds between queries.

4. Include an additional Backend Listener in the Thread Group.

Put the URL of your Grafana Cloud instance into the InfluxDB setting.

For your Grafana Cloud instance, enter the proper credentials for the InfluxDB User and InfluxDB Password.

List the risks involved in performance testing

To outline the dangers associated with performance testing, let’s ask ChatGPT.

The reply from ChatGPT is absurd.

Risks associated with performance testing include the following:

Incorrect test design: If the test design is inappropriate or does not truly reflect how the application is actually used in practise, the performance test results may be unreliable and fail to pinpoint the program’s actual performance bottlenecks.

Insufficient data: The findings of the performance test may not be reliable and may not pinpoint the program’s genuine performance bottlenecks if the test data is not indicative of the real-world usage of the application.

Inadequate test environment: The findings of the performance test may not be reliable and may fail to pinpoint the application’s actual performance bottlenecks if the test environment is not reflective of the production environment.

Unforeseen system behavior: Performance testing may uncover unexpected system behaviour, such as increased resource utilisation, capacity concerns, and other scalability issues that were not anticipated during development.

Overload and crash: Application unavailability, data loss, and other undesirable effects might come from unanticipated overloads and failures brought on by performance testing of the application or testing infrastructure.

Performance testing may reveal sensitive information, posing a risk to data security and compliance.

Lack of test Automation: Running performance tests manually will take a lot of time, be prone to mistakes, and make it hard to repeat the test results.

Inadequate monitoring and analysis: If the monitoring and analysis of the performance test results is insufficient, the underlying performance bottlenecks of the application might not be found, and the test findings might not be accurate.

Cost: Conducting performance testing can be a time-consuming operation that calls for a large investment in manpower, technology, and software. This is another ChatGPT Use Cases For Performance Engineers.

Lack of Standardization: Performance testing can be prone to errors and inconsistencies without a consistent procedure and test methodology, leading to unreliable test findings and the inability to compare test results across multiple tests or test runs.

Build a performance testing tool

Let’s use ChatGPT to create a Python performance testing tool (python is slow, isn’t it?).

Here’s what I’m asking:

• Using the HTTPX and Panda libraries, please assist me in creating a Python performance testing tool.
• I require the following attributes:
• Ability to test numerous HTTP requests at once; Handling of failures; Parameterization of Test Data Input; Analysis of Test Results; Generation of HTML Report ChatGPT’s response:
• Here is an illustration of how the HTTPX and Pandas libraries can be used to create a performance testing tool in Python:

1. Start by installing the necessary libraries:

• pip
• Instal pandas http
• Matplotlib
• Create a function that uses the httpx library to send HTTP requests.
• The URL, method, headers, and content should be passed as input parameters to this function, which should then provide the response.
• Include httpx
• url, method, headers, content, and def make request
• response is same to httpx.request(method, url, headers=headers, body=json)
• unless, return answer
• zan exception:
• zrint
• ‘Error:’ (e)
• Make a function that parses the test data after reading it from a CSV file.
• The test data should be returned by this function as a list of dictionaries.
• bring in pandas as pets
• df = pd.read csv(file path) for def read test data(file path)
• df.to dict(orient=’records’); test data
• deliver test data
• Make a function that utilises many threads to do the performance test.
• The test results should be returned in the form of a list of dictionaries by this function, which should take the test data and the request function as arguments.
• concurrent.futures import

With concurrent.futures.ThreadPoolExecutor() as the executor, def run performance test(test data, request function): results = [] and futures = [executor.submit(request function, data[‘url’], data[‘method’], data[‘headers’], data[‘body’]) for data in test data]

Response = future.result() results for future in concurrent.futures.as completed(futures).

Results are returned by appending “url” to response.url, “status code” to response.status code, and “elapsed time” to response.elapsed.total seconds().

Utilizing the Pandas library, create a function to analyse the test results.

The test results should be passed as an argument to this function, which should then return statistics like the mean, median, and standard deviation of the response time.

• bring in pandas as pets
• df = pd in def analyze results(results).
• Statistics for DataFrame(results) = df.
• describe()
• return figures
• Make a function that produces a test report in HTML format.
• This method should utilise the matplotlib package to generate a line chart of the reaction time after receiving the test results and statistics as parameters.
• The graphic, the statistics, and the specifics of the test findings should all be included in the HTML report.
• from matplotlib.backends.backend agg, import matplotlib.pyplot as plt.
• from matplotlib.figure, import FigureCanvasAgg as FigureCanvas
• import io import base64 for a figure

Final Thoughts

I haven’t personally tested the bits mentioned above. However, I’ve been using ChatGPT to create short snippets of Groovy, Python, and Bash. ChatGPT occasionally responds incorrectly, but it is simple to deceive.

A newborn cannot be expected to run the day of its birth. Let’s harness AI’s potential to create a better, more prosperous future for present generations as well as ourselves.

We must embrace this ground-breaking AI chatbot website that is here to transform the world with all of our might and tenacity.