Test automation vs. Autonomous testing: What sets them apart?

We’re all now seeing the unprecedented shift in software quality assurance. With years, from tech behemoths to small businesses — all now understand that even minor defects can escalate into significant issues. Not convinced?

Recall the 2012 Knight Capital Group incident, where a software glitch led to unintended stock trades and a USD 440 million loss in just 40 minutes. Epic fail, innit? The cause is inadequate testing and old-fashioned approach.

You may ask, what would they do if there wasn’t any AI in testing at that time? But automation was. That was a first step toward innovations, using scripts and tools to execute predefined tests. However, with advancements in artificial intelligence and machine learning, we now have autonomous software testing. Traditional testing and AI-powered one both aim to improve efficiency. Yet, they represent different stages in the evolution of software testing.

Test automation is a GPS device: You input the destination, and it provides the route. But this move requires your attention and action. Autonomous testing, on the other hand, is like a futuristic robotic taxi that knows the destination and navigates traffic, adapts to road conditions, and optimizes the journey without the “right-hand obstacle”.

Let’s collate test automation vs autonomous testing and uncover the differences between them. The presented solutions like Owlity are just examples of the capabilities of autonomous testing. So, keep your eyes open.

What is test automation?

Imagine you are a chief. You are cutting, boiling, preparing the food, and generally orchestrating the kitchen staff. But the other day, you found three robots, one for each process: cooking, curating, and checking the result. That “robots” are traditional test automation in a metaphorical meaning. In other words, specialized software tools execute pre-scripted tests before the app is released into production.

The primary goal: To automate repetitive and time-consuming tasks within the testing process and free QA teams for improving overall efficiency. And yes, automated tests can be run multiple times with minimal effort.

The 2022 State of Testing report: Only 24% of companies didn’t use test automation at all. 76% do.

Where was the beginning?

In the early days, testing was predominantly a manual process. During the '80s and '90s, apps drastically became more complex, and the limitations of manual testing became apparent. This evolution started off the development of automated testing tools.

Key milestones:

→ Early scripting tools: Simple scripting languages (just to automate basic test cases) required testers to have programming knowledge and were often inflexible.

→ Dedicated frameworks: The late 1990s and early 2000s were marked by the birth of frameworks (JUnit and NUnit); this facilitated automated unit testing within development environments.

→ Web application testing: Internet and software evolved, so there is no wonder at the beginning of the new era — automated testing of web browsers. Selenium (2004) began software testing transformation.

→ Integration with Agile and DevOps: Since the 2010s, Agile methodologies and DevOps practices have been evangelizing continuous integration and delivery. These terms have become synonyms of speed cycles and better quality, and hence, high user satisfaction and stable revenue.

The reasons for evolution:

• Handle increasing software complexity: Manual testing could not scale effectively with the growing intricacies of modern applications.

• Accelerate release cycles: Organizations needed to deliver features faster to stay competitive.

• Improve accuracy and reliability: Automated tests reduce human error and provide more consistent and dependable results.

Traditional test automation key features

1/ Increased speed

Manual tests and automated ones are akin to the famous fable “The Hare and the Tortoise,” who competed in speed. Suppose, you guessed it right — automated tests are a Hare in this case. They are significantly faster, despite some limitations.

For instance, manual QAs would complete regression test suites for days; automated tests will run overnight or even within hours. The benefit is clear: more tests in less time.

2/ Improved coverage

This point stems from the previous one: the more time saved — the more complex scenarios and large data sets you can perform. Consequently, no defects go unnoticed.

3/ Consistent and reliable execution

Similarly to the autonomous test platform, automated tests perform the same steps precisely without deviation. This consistency ensures that test results are reliable (as far as there are no mistakes in prescripted test cases).

4/ Cost savings

We should add: over time, because the initial setup of automated testing requires some investments in tools and scripting. But let’s take a look at the long-term costs — they will be lower compared to manual testing. Reusable test scripts and reduced labor hours lead to significant cost savings, especially in projects with long maintenance cycles.

5/ Faster time-to-market

Automation accelerates the testing process, enabling quicker feedback to developers and reducing the duration of testing phases. This speed facilitates more frequent releases and updates without harming the software quality assurance postulates.

What is autonomous testing?

It is an advanced approach to software testing that leverages artificial intelligence (AI) and machine learning (ML). The system creates, executes, and adjusts test cases without your continuous participation.

Just compare: Traditional test automation relies on predefined scripts and requires manual updates when the application changes, and the autonomous testing system uses machine learning testing to learn from the software's behavior and dynamically adjusts its testing strategies.

AI algorithms analyze application code, user interfaces, and usage patterns and generate test cases that might not have been anticipated by human testers. They can also self-heal by adapting to changes in the application, such as updates to the user interface or alterations in the codebase.

Take a conditional example:

The fintech niche, a leading global bank, and a complex suite of mobile banking applications. The industry is strictly regulated and requires keeping thorough norms and laws. From this perspective, implementing AI in testing is almost the only way to reduce regression testing time and increase test coverage without overwhelming the existing QA team and testing budget.

The wise step as it improves software quality and accelerates release cycles, giving bank teams a competitive advantage in the industry.

AI-powered testing key features

1/ Intelligent test generation

AI and ML algorithms analyze application code and user behavior and generate test cases themselves. Intelligent enough to ensure comprehensive coverage, considering edge cases that are often overlooked.

2/ Self-healing

When applications change, traditional automated tests often break, requiring manual updates to test scripts. No way this happens with autonomous testing since these tools have self-healing capabilities: The AI system detects changes in the application and adjusts the tests accordingly without tasking QA Engineers.

💡AI testing tool saves months for you by leveraging AI/ML and designing comprehensive test cases. Owlity, for example, doesn’t even require QA experience.

For example, if a button's identifier in the user interface changes, the AI recognizes the alteration through other attributes or context and updates the test.

3/ Learning and improvement

These systems continuously learn from already completed tests and continue to learn with each execution. By the way, constant application changes also influence the systems’ “memory”. Even though many modern AI models are supposed to be mediocre yet not completely incompetent students (if we trust Terence Tao, a UCLA professor’s words), they refine their actions based on new data and become smarter.

4/ Accuracy

The AI power of autonomous testing achieves higher test accuracy, despite significantly wider test coverage. Machine learning simulates user behaviors across different segments, platforms, and environments based on the previous train data.

Just imagine, this is a colossal amount of information many people can’t even comprehend. This is why ML could serve better in QA — it also creates test scenarios that are difficult to anticipate manually.

5/ Faster time-to-market

Autonomous testing supports continuous integration and continuous deployment (CI/CD) pipelines by providing real-time testing and feedback. This accelerates the development cycle and enables faster delivery of high-quality software.

The AI system can execute tests immediately upon code changes, providing instant feedback to developers. This rapid turnaround reduces the time spent in the testing phase and allows for more frequent releases.

Limitations of autonomous testing

Nothing is ideal, and autonomous testing isn’t an exception. For example, companies may need some time to understand the technology and evaluate whether it fits their specific needs and workflows since AI testing is a relatively recent development. Also, the initial setup and training models with relevant data may take the effort.

Key differences between test automation and autonomous testing

When to choose test automation vs. autonomous testing

How about an extra 20 min a day per employee? And how about less security and compliance problems? After all, how about the end result of your work — the quality of your software? These questions may seem childish, but your answers are vital. So, before going all in with a testing solution, take into account the following points.

Project complexity

Are you a small project (under 20 people and the only simple app)? Therefore, traditional test automation might be sufficient. In predictable and relatively stable environments, the functionalities are limited, and changes are infrequent, so manual test case updates are not a big deal.

> Simplicity of requirements: Smaller projects typically have a narrower scope, and QA teams simply don’t need advanced AI capabilities.

> Predictable changes: A stable feature set keeps test scripts' maintenance overhead low, reducing the need for self-healing or adaptive testing mechanisms.

For instance, a small enterprise developing an internal tool with well-defined features might find that traditional test automation meets their needs efficiently. The investment in autonomous testing may not be justifiable given the project's scale and complexity.

Scale and frequency of releases

For larger, more complex projects with frequent releases, autonomous testing becomes increasingly valuable. Applications in Agile or DevOps environments often have continuous integration and deployment cycles, requiring testing approaches that can keep pace with rapid changes.

> Continuous testing and adaptation: AI and ML generate and adapt test cases right after every code change; no need for wide QA staff.

> Risk mitigation: Frequent updates increase the likelihood of defects in production. Again, it forces you to adapt tests lightning-fast. Autonomous test platform does it itself.

ℹ️ The recent MoneyGram data breach (50 M people in 200+ countries) showcased the critical role of continuous testing in mitigating security vulnerabilities and protecting sensitive information. Hackers stole names, phone numbers, postal and email addresses, DoBs, and even some Social Security numbers (like driver's licenses). Prevent with OwlityAI.

Healthcare and any other companies handling sensitive data or operating in highly regulated industries could do with autonomous testing as it allows them to rapidly adapt to code changes and identify potential vulnerabilities.

Resources

• Limited AI expertise: Without in-house AI or machine learning specialists, it might be challenging to implement and maintain AI testing tools. Conduct a software testing comparison, and begin with traditional test automation if it’s your case.

• Budget constraints: Autonomous testing tools often involve higher initial costs for licensing, infrastructure, and training. Yet, as we discussed in the table above, the long-term ROI overcovers all the initial costs.

• Scalability considerations: Planning to scale your operations? Autonomous testing can provide long-term benefits ensuring a smooth future. Efficiency, reduced maintenance efforts, and improved software quality — tangible perks of hard initial decision.

Companies that can invest in AI technologies and are willing to adopt advanced testing methodologies stand to gain substantially from autonomous testing.

> Investment in AI capabilities: Organizations with data science teams or the ability to hire AI experts can fully leverage the benefits of autonomous testing tools.

> Strategic advantage: By adopting autonomous testing, companies can enhance their testing efficiency, reduce time-to-market, and improve product quality, providing a competitive edge in the market.

Bottom line

Test automation vs autonomous testing emerged as a dilemma for business owners. Both improve efficiency by automating repetitive tasks and reducing manual effort. But what to do, if you grow fast, and so do your apps?

Then, your choice may be autonomous software testing. AI and machine learning features of this approach create, execute, and adapt tests independently. Consequently, you have increased efficiency, flexibility, and long-term cost savings.

This is not to mention competitive advantage: Deloitte calculated that companies with only GenAI tools save up to 20 minutes per day per employee. If you can use saved time strategically, you will always be three steps ahead.

With Owlity features (intelligent test generation, self-healing, adaptive learning, and others), you will get an enhanced testing strategy and deliver high-quality software.

Not sure whether Owlity can transform your testing process and give your organization a competitive edge? Request a demo and check.