We like to marvel at how technology keeps changing. Yet, some things stay the same. Some teams struggle with an avalanche of regression tests. Some with the complexities of scaling. Some with how difficult hiring talent has become. And the lucky ones grapple with all of them and more. The progress of tech creates new challenges. It’s like the great filter: to survive and thrive, you must overcome the barriers in front of you.
Today, we discuss a solution that can help you with that — automated test case generation.
Automated test case generation (ATCG) is the use of tools and algorithms to create test cases. With the traditional approach, specialists go through specs, user stories, requirements, etc. Then, they identify relevant test scenarios. And finally, QA engineers create test cases themselves, writing them by hand.
When done manually, this process can take around ten minutes. But per one test. So, this adds up significantly. And given that for complex products test case creation time can stretch beyond one hour, generating test cases can become a bottleneck. Though it’s a critical piece of QA.
It can be improved, however, with automated test case generation using AI or specialized tools. Here’s how it works.
Once you have this process organized, test case generation will take seconds.
Yes, speed is the most pronounced advantage of automated test case generation software. But it’s not the only one.
ATCG tools handle repetitive tasks. Combing through data, identifying scenarios, and writing tests becomes much faster. And that means that your team can start testing much sooner.
Test case automation lets you expand your scope. It can create a ton of test cases with tiny variations if you’re looking for the best coverage possible. It can suggest unlikely cases so that you don’t miss anything. It can focus on perfecting critical flows. These lead to thorough testing and minimized production bugs.
Tools take care of routine, time-consuming duties. And your team has more time to work on complex scenarios or product improvements.
AI automated test case generation structures and properly documents all the inputs it works with. Plus, you can use features that allow for visualization, guide-creation, etc. All this info can be reused for future releases, onboarding, and training.
Automation test case software can update and regenerate test cases systematically. So, every time your product changes, it ensures that no regressions slip through. You don’t have to pause work and go back in time to rewrite tests. The tools do this for you.
You don’t have to keep expanding your team as your product evolves. AI driven test case automation will create new test cases whenever features are added or changed. It can update existing scenarios. And it won’t slow down testing as test generation even with large datasets is quick.
Another great thing about ATCG is that you can keep improving it. You’re likely to need an expert to maintain and refine the tool you’re using. But it’s beyond worth it as skilled upkeep will make your tool more accurate and adaptable, teach it to better handle complex scenarios, and refine its ability to secure quality coverage.
Despite the prospect of incredible benefits, test case generation should be used carefully.
First, you’ll need specialized expertise — a person to set up, implement, and guide automated test case generation (AI or no AI). A tool won’t work on its own. And without maintenance, it can turn useless pretty quickly. Or worse, it can harm your product with test overload, superficial coverage, inaccurate outputs, etc.
Second, ATCG has its limitations. You should know them to not rely on tools for tasks they simply can’t execute or are bad at.
You know that automated testing services are powerful. But you don’t automate everything and forget about manual QA. AI is very helpful. But you don’t let it run wild or entrust business-critical tasks to it. The same applies to test case generation tools. They have their strengths and weaknesses. So you ought to be strategic about their application.
Automated test case creation has drawbacks we’re all very familiar with. They lack human experience and judgment. And they need monitoring. These aren’t the worst things to exist. But they do call for one must-have — skilled specialists.
An expert will recognize a tool’s mistake and tweak it so that the failure doesn’t appear again. An expert will step in when a tool can’t properly handle a task. An expert will set up a process that makes a tool most productive. Simply put, to make good use of ATCG software, you need people who know how to realize their potential and organize related procedures, like automation or CI\CD integration.
If that’s not something you have at the moment, consider QA outsourcing services. You’ll get instant access to a professional with the exact skills you need. And you’ll enjoy the perks of ATCG much sooner.
Given the cons, automated test case creation works best for projects where speed, volume, and consistency take precedence. If you work in a fast-paced environment or have a complex project with a lot of moving parts, logically, you’ll need something to help you keep up.
So, plug in ATCG tools when:
Stick with manual test case writing when:
Keep in mind that this division doesn’t mean that you’re stuck with either this or that option. They don’t exist in a vacuum. And so, manual and automated test case generation should work in combination, not separation.
If you go for ATCG tools, you’re not obligated to go all in. Use automation where it’s useful. Use manual efforts where they matter. Strive to keep up this balance. If there’s too much on one side and too little on the other, your quality will start to crack soon enough.
Another aspect where you’ll need to guide your ATCG tools is how they create tests.
If you task a person with creating tests, they first get to know product info: requirements, code, user stories, etc. — what’s available and what’s needed for your testing goals. Based on your goals and their experience, a specialist may focus on priority areas. These can be functional checks, working with edge cases, or something else. Then, the person identifies scenarios to test, defines test conditions, and begins writing the test cases.
This is a simplified process overview. But it’s enough to explain the purpose of automated test case generation techniques. They explain to the tool how you want it to generate tests. Let’s illustrate this by reviewing the most common techniques.
Using this technique means letting your tool play around. There’s little to no structure, and specific directions are rare (unless you want the tool to center on particular areas). Basically, the software performs random actions, like clicking buttons or filling out forms. This method lets you try lots of different inputs, see if anything breaks, and catch unexpected bugs.
This approach first takes in the model of your software behavior. It learns how your product should act in different situations, such as logging in or making a purchase. The tool then uses the map to create tests that follow paths a user could take, making sure every step works correctly.
Requirement-based testing focuses on what software should do. For example, it sees a rule like “the system must show an error if the password is wrong”. Then, the tool generates tests to check that this rule is correctly implemented. This technique is best for making sure your app sticks to its instructions.
This approach is also straightforward. An ATCG tool reads written instructions or reports and turns them into tests. For example, let’s say a document says “a user cannot check out without entering a shipping address”. Then, the tool will create a test that tries to check out without an address to see if the system responds correctly.
In keyword-driven testing, the tool works with predefined actions like “click login”, “enter username”, or “submit form”. These are like building blocks. And they can be arranged in any sequence. The tool combines these actions in different strings to create full test scenarios. For example, it might first try “enter username > enter password > click login”. Then, it’ll try a different order to cover different workflows.
In data-driven testing, the steps of a test stay the same, but the input values change. For instance, the test might always follow the steps “enter username > enter password > click login”. But it’ll run multiple times using different usernames and passwords. This lets you test many variations quickly without rewriting the test each time.
AI studies past bugs, user actions, and system behavior. With all the collected data, it creates tests that are likely to find problems. Over time, it learns which types of tests are most useful, offering more accurate scenarios.
Search-based testing uses smart exploration to generate tests that are more likely to uncover bugs. It searches for input values or sequences that cover many scenarios or trigger unusual behavior. Briefly, this technique looks for the best ways to find issues.
This approach introduces small, intentional changes in the code, like flipping a condition or changing a calculation. Then, it generates tests to see if the system detects the error. The goal isn’t solely to find mistakes, but to check whether your tests are strong enough to find them.
Keep in mind that automated test case generation techniques are commonly combined. It’s actually preferred. A mix of two or more approaches increases your coverage, improves efficiency, and minimizes blind spots. For example, you could combine requirement-based testing to secure correct behavior and random testing to locate edge cases. You can also plug in mutation-based testing to see how well a technique or a few of them mixed work.
So, you’ll need experts who know how to successfully join techniques and make them function together rather than doing their own separate tasks. You’ll need the same when you’re selecting ATCG tools. They are usually categorized by:
There are also automated test case management tools, which help organize, track, and execute test cases efficiently. Don’t overlook this software. With ATCG, you’ll operate with a lot of tests. And you wouldn’t want them to be single-use. Plus, you can rely on documented and clean documentation for future efforts, onboarding, and training.
As we’ve established, automated test case generation is extremely useful. But it’s not magic. For it to work for you, you first need to make sure you have what it needs to be a success. That includes: structured processes, smart use of ATCG techniques, tool setup and management, and integration with existing workflows. The good thing is that to get all that, you need just one thing — skills.
Skills and quality are what we specialize in.
QA Madness’ experts don’t simply establish productive ATCG. They make it a part of the bigger picture that contributes to your business. We customize our approaches to fit your needs and make sure our efforts support your ultimate goals.
Automated test case generation, like many incredible solutions, comes with one big issue. We want to use the benefits it offers so badly that we sometimes forget that good things need effort. That’s how we end up with takes like “it’s not even that useful” or “we won’t try this as something similar didn’t work out in the past”. ATCG doesn’t instantly provide faster delivery, lower costs, and better quality. It has the potential to do so. If you do it right.
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