TL;DR: We are often asked why we need that much time to recheck load test scripts. So, here is our explanation in ten sentences or less.

“Why is the script broken? We haven’t changed anything?” A load test script can be broken in two ways. It breaks clearly, hitting you with an exception or assertion and giving you explicit errors. It’s just incorrect when it appears to pass but is subtly flawed, leading to misleading results.

“But we haven’t changed anything in the UI!” Load test scripts don’t magically adjust. You can’t assume load testing is UI automation on steroids because UI tests are resource-heavy due to our modern browsers. That makes the tests inefficient and costly for simulating many users, whereas load tests use lightweight, lower-level simulations designed for true scale.

“So, where did this change occur?” Scripts break due to changes in HTML/CSS, JSON, required data, or application flow, while they become incorrect because of optional data changes, wrong requests/order, or outdated data.

The secret to lower script maintenance lies in communicating changes with your performance testers early and in validating all data. The less vague an API is, the easier it is to keep the scripts up to date. This way, you’ll ensure your load tests are a true reflection of reality, not just a green checkmark!

Introduction

It’s a question that echoes through many development teams: ‘Why do we need to touch the load test scripts? We haven’t changed a thing on the UI!’ This common query stems from a perfectly logical assumption – if the user interface looks the same, surely everything behind it is too, right? Unfortunately, in the complex world of modern applications, what you see isn’t always what you get, and a static UI can hide a whirlwind of activity that directly impacts your performance tests.

Let’s talk first about what can really mess up your load testing scripts. It’s super important to know the difference between a script that’s completely broken and one that’s just plain incorrect.

• Broken scripts: These are the ones that just won’t run through at all. If it’s a JUnit test, for example, you’ll see a big fat failure message. It’s dead in the water.
• Incorrect scripts: These are trickier. They’ll actually run all the way through and show a “green” result, but they haven’t done what they were supposed to. They’re silently misleading you, and they are the main reason why load test script validation isn’t just run and tell based on outcome.

Test Automation vs. Performance Tests: Not the Same Thing!

Because people often mix these up and assume that a load test script is as easily maintainable as a test automation script, here are the key differences.

• Test automation is usually about checking if something works. Think of it as replacing manual testing or extending its reach so you can get feedback faster. These tests are often UI-based and interact with a real browser or an app.
• Performance/Load Tests, on the other hand, are about simulating how users behave, but at a much lower level than the UI. This lets you directly control things like the type of calls, data, and even filtering. You also get rid of a direct dependency on a real browser.

Now, you might think, “Why not just scale up my UI automation tests?” Good question! But here’s why that typically doesn’t work.

Modern browsers are hardware hogs. They need multiple CPUs, 512 MB or more memory, and often a GPU just to run smoothly. Trying to run huge tests with actual browsers chews up too many resources, making it expensive and unreliable to test higher traffic.

Plus, running a UI test at scale would be a massive waste. You’d be rendering the same UI a million times over without learning anything new about performance. Browsers are also a pain to control remotely, especially when you need to filter or tweak requests to hit (or avoid) certain resources. That filtering can lead to all sorts of problems because of JavaScript dependencies or rendering quirks when third-party calls are skipped during a load test. And frankly, browsers are terrible at telling you when they’re truly ‘ready’ because so much is happening asynchronously. Modern websites are almost never silent, always doing something in the background, making that “ready” state even harder to pin down.

So, to sum it up: performance test scripts are not test automation scripts. The latter are typically UI-based, work at a higher level, and aren’t as sensitive to the nitty-gritty, low-level changes like more or fewer requests, or little parameter tweaks. Performance scripts live in that nitty-gritty world.

What Makes a Script Break?

These are the things that will make your script crash and burn:

• HTML Changes: Your CSS selectors or XPath expressions suddenly don’t work because the HTML changed.
• Required Data Changes: The data you have to submit has changed, and your script isn’t sending the correct stuff.
• Flow Changes: The application’s flow changed (like a checkout process becoming shorter or longer), and your script can’t follow it any longer.
• Invalid Test Data: You’re using test data that’s just plain wrong, and the system can’t handle it at all.

What Makes a Script Incorrect?

These are the sneaky ones that run but give you bad intel:

• Optional Data Changes: The data you can submit has changed. The script runs, but it’s not truly reflecting real user behavior.
• Extra/Missing Requests: Your script is sending requests it shouldn’t, or missing requests it should be sending.
• Wrong Order of Calls: The calls are happening, but not in the sequence they would in a real user journey.
• Invalid Test Data: The data is technically valid, but no longer represents the state of the system. Because there is not enough validation in the system under test, things don’t break but rather go unnoticed.

Of course, these are all just examples. While a JSON format change might break scripts for you, it might just lead to incorrect testing for someone else.

How to Keep Your Scripts from Going Haywire

To avoid this constant headache between your scripts and the actual application, you need to bake script maintenance right into your development process. The biggest help here is communication. If performance testers know about feature changes, they can figure out what needs to be done. This means less scrambling to review every script all the time.

Here’s how to tackle those changes:

• Talk About Changes: Especially when the front-end will see new or removed requests, logic updates, or changes to the data being collected or sent. Keep performance testers in the loop!
• Disable Old Functionality: When something’s removed from the front-end, make sure to disable it to make it impossible to work. This will make your scripts fail if they try to hit old endpoints, which is a good thing – it forces you to update the scripts.
• Verify All Required Data: Always verify all the data needed for an action; don’t just leave things optional. This doesn’t just help your performance tests; it also boosts functional quality and security.

Or in English: Every change of the UI for already performance test scripted functionality should yield to a breaking script state. This requires the performance tester to validate things carefully and the application engineer to apply boundaries to the application that communicate clearly what is needed or not desired.

And yes, it is still possible that you cannot set these clear boundaries because the application itself might not know about certain states, or the APIs are not yours so you depend on their overflexibility.

Conclusion

Load testing scripts are different from normal automation, with unique requirements for creation and maintenance. Recognizing the fundamental differences between “broken” and “incorrect” scripts, and between performance testing and UI automation, is vital for achieving accurate and reliable performance insights. By integrating script maintenance into the development process through proactive communication and robust practices, teams can ensure their performance tests remain effective, reflecting the true state of their application under load.

So, there you have it. Load testing scripts are a different beast, and understanding their quirks is key to getting good performance insights. Keep these points in mind, and you’ll be much better equipped to handle them.

P.S. There is an option to run sensible but still resource-intensive load tests with XLT: It supports load testing with real browsers at scale. This is perfect for a blend of test automation and load testing. Of course, you likely are not going for hundreds of users, but rather a small set for a regression and sanity check. First line of defense, so to speak.

P.S. Depending on the framework and concepts you use, going headless with your application probably means a significant increase in scripting effort.