Even though AI is considered to continue learning and expanding its intelligence and awareness through machine learning, it’s still pretty far from perfection. And anyway, AI in software testing will be an integral part of the process. But how, when, and why? Let’s look at how using AI can help QA engineers work smarter and more efficiently.
Why should software testers use artificial intelligence?
QA professionals have always been inclined to use test automation tools to aid them in their testing efforts. As a tool, AI in software testing can help better understand the business problems they’re solving, find bugs faster and more consistently, improve the quality of their testing, and save time by automating menial tasks.
However, manual software testing has limitations, and many repetitive tasks can’t be automated because they require human intelligence, supervision or judgment. As a result, companies are looking at AI to improve the quality of their software testing efforts and address some of these limitations.
- It can learn from past experiences and apply what it learns to similar situations;
- It can be used as a tool that helps with regression testing, which means it helps find bugs before they happen again;
- It can analyse data faster than humans ever could, resulting in more thorough testing of products or services;
- It can automate repetitive tasks like bug reporting or test case management.
The first thing that QA engineers use AI for is test automation. This can be very helpful in ensuring that the software they are working on has been adequately tested and can be used by customers without any issues. When they use AI to automate their tests, it makes it easier for them to run these tests repeatedly. This can help ensure that they find any bugs quickly before they get out into the world where they could cause problems for customers or even worse damage.
Another way that QA engineers use AI is by using machine learning models to help them identify problems with the software being tested. Machine learning models can analyse data from past tests as well as other similar programs. So that they can identify patterns within those programs, which could indicate potential problems down the road when users start using them in real-life situations outside of controlled environments.
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The limitations of manual testing and how AI could solve it
Letās look at the key limitations manual software testing creates for QA teams:
- It’s slow and labour-intensive, which makes it difficult to meet tight deadlines.
- Human errors are more likely compared to automated software testing, increasing the risk of missed issues.
- Test coverage is often limited, especially with large, complex applications. It leaves some areas untested.
- It struggles with large, complex datasets, which are becoming more common in modern software.
- Scaling is a major challengeāas your application or development team grows, itās unrealistic to expect manual testers to simply double their efforts. Manual QA lacks the capacity to quickly adapt to rapid growth, making it harder to maintain quality at scale.
The more you rely on human expertise, the more problems occur, except when it comes to decision-making and intuition (this is also debatable). And AI is the cornerstone to deal with these challenges.
With AI, you can:
- Automate repetitive tasks, speeding up the software testing process
- Analyse data patterns, leading to better test coverage and complex scenario management.
- Ensure consistency across tests, even as the code evolves
- Predict potential defects early
These lead to more efficient, accurate, and reliable testing outcomes for QA teams. However, the use of AI in software testing is not a necessity in all cases. When is it the best time for it? Letās break it down next.
When to use AI in software testing?
Let’s start here with another question that probably arises right after you are done with the first part of the article ā what is the difference between test automation and AI in quality assurance testing? Letās find out before you decide to prepare the QA team for the test automation without knowing if you actually need it.
So, itās definitely not about using an automation test tool. You still need a human intellect to incorporate and maintain all creative aspects such as business strategies, evaluating personal progress, and correcting workflow concerning your employee’s affairs. And AI possesses the biggest value in leaving time for you to deal with this “creativity”. It removes the burden of monotonous and redundant actions from developers and testers.
As it is now, not all automation testing tools can carry out a significant stack of tests and process results. Additionally, the launching of tests is still under human supervision as test automation tools need to know what tests to start or they run all at once.
But with an AI bot, it would be possible to review the current statuses of your tests, recent changes in development and code coverage changes, and just then decide on what tests are necessary to start.
AI techniques applied in QA can speed up SLDT ten times faster. For example, it helps with warnings and false positives.
Automated testing is cool, but it still can bring an enormous amount of false positives about events that are not actual bugs or multiple warnings that usually don’t happen when manually testing. Such a thing “kills” development and QA teams as they have to repeatedly review an existing code to eliminate possible problems. Imagine their frustration when they find out that it was a false positive?!
What can AI do for them? The solution can be creating a classifier based on AI. And here is the literate quotation of Igor Kirilenko, Parasoft’s VP of Development.
What is autonomous AI testing and why do you need it?
Now that we know when to use AI in testing, letās dive into autonomous AI testing.
Autonomous AI testing means AI is doing the heavy lifting by creating, running, and even adapting tests on its own, without human input. Itās going beyond automating tasksāit also includes learning from the tests it runs, adjusting and improving over time.
Why is this a game-changer? In QA environments, manually updating test cases is time-consuming. Autonomous AI testing solves this by automatically adjusting to changes, and keeping your tests relevant and up-to-date.
This approach further saves time, boosts accuracy, and handles complex scenarios that would be hard for manual testers. Before, we were talking about using AI. With Autonomous AI testing, you fully rely on AI.Ā
But of course, this approach is too risky, as you will definitely need human input, especially in enterprise environments where each mistake could be costly.Ā
Integrating AI into your testing processes is a less risky approach than making AI your whole software testing process. Weāve briefly touched on this in the above section, letās break it down in detail.
"This classifier is based on results from previous classifications of static analysis findings in the context of both historical suppression of irrelevant warnings and prior prioritisation of meaningful findings to fix inside the codebase."
Another example is integration related.
UI is one of the pillars for creating high-conversion websites. And all developers are pursuing automated testing of UI to simplify and speed up this process. And for this purpose, they usually prefer using the Selenium framework. However, Selenium could be better. It still runs some issues with testing maintainability and stability. To avoid this problem, developers can incorporate AI and ML as self-healing methods at runtime to address troubles associated with UI test execution. Running tests capture important information about UI imperfections and find the solution from a historical perspective.
Key benefits of using AI in software testing for TOP QA engineers
AI can be a great boon to your QA team, and it’s not just hype. Here are the top 3 ways AI can make your QA process better:
Test consistency
The presence of artificial intelligence can level any kind of disagreement in the team, as well as prevent testers from burning out during the same type of testing.
Imagine that you are maintaining a huge project with hundreds of thousands of code lines and a large front end and back end. The number of tests can simply “hurt the psyche” of any professional tester ā in principle, they can already imagine how much monotonous and uninteresting work they will have to cope with.
The use of AI in quality assurance, especially in these types of projects can not only accelerate goal achievement but also reduce burnout factors and anxiety due to tight deadlines and overwhelming workloads and eliminate contradictions in the team.
Better defect tracking
The main advantage of AI in defect detection is that AI can analyse errors. That is, at the end of testing, we can get a “description of the alleged offender”.
For developers, this is important because it allows you to discover other vulnerabilities in the product that may contain similar problems and get rid of them immediately.
However, one can go further and use AI to fix these bugs without the explicit presence of developers in this process.
Wider test coverage
AI can push the test coverage of a product very far forward, the scope of which cannot be provided by either automated or manual testing. Because AI āācan view the necessary information and internal states, it can decide where additional testing needs to be done.
How can you overcome challenges in AI-driven software testing?
Okay, we admit it; we have been teasing you a lot regarding AI being imperfect. While AI can revolutionise software testing for your team, it is obvious that it comes with a few challenges. Letās break down what youāll face and how to handle it.
Steep Learning Curve
AI tools for test automation will constantly require your team to learn new skills. So youāll need a solid understanding of how AI works. For instance, training AI to detect bugs in specific scenarios takes time and expertise. Without this knowledge, you canāt fully use AIās potential. This is why investing in training and onboarding is crucial for your team to succeed.
Troubleshooting AI Tests
Debugging AI-driven tests can even be more complex than traditional tests. When a test fails, it can be difficult to figure out what went wrong. Is it the AI’s logic? The data? Or is it the software itself? Understanding AI behaviour will be key to fixing these issues quickly and effectively. Only then, can you ensure your tests run smoothly.
How do you maximise the power of AI technologies in software testing?
Several AI features can enhance your testing process, but there are some unignorable ones. Hereās a quick look at whatās out there.
AI-Powered Script Creation
With AI, you can generate test scripts automatically based on how users interact with your application. This saves you time and effort. Instead of writing scripts manually, AI watches how users navigate and creates tests from that data. This way, you can ensure your tests are grounded in real user behaviour, keeping tests relevant.
Test Case Optimisation
AI revolutionises your testing strategy by zeroing in on what really matters. Instead of running through a long list of tests, AI helps you examine past results and highlight high-risk areas in your software that need your attention. This means that when time is tight and you need to make quick adjustments before deployment, AI allows you to pivot effortlessly.Ā
Letās imagine you discover a bug in a feature thatās crucial for user experience. AI helps you focus your testing efforts on that specific area without sifting through irrelevant cases. So you save time and strengthen the overall quality of your product, without compromising anything.Ā
Smart Test Execution
AI can also help your team decide the best time and order to run tests. It analyses past data and efficiently organises the execution process to prioritise critical tests. In fast-paced environments like CI/CD pipelines, this approach is crucial. It ensures the most important tests are run first, helping you catch issues earlier and reduce bottlenecks.
For example, letās say youāre part of an e-commerce platform that processes numerous transactions daily. You could use AI to assess past test results and identify high-risk areas such as payment processing and user login. With this prioritisation, your team can catch potential issues earlier and prevent delays in release. This proactive approach helps streamline your testing efforts and enhances the overall reliability of your platform, leading to a smoother shopping experience for your customers.
Self-Healing Test Scripts
Self-healing test scripts are a significant advancement in maintaining the integrity of your automated tests. Whenever there there is a need for application testing after small changesāsuch as a UI element moving or being renamedāAI helps you adjust the associated test scripts automatically. This reduces the frustration of dealing with broken tests that arise from minor updates.
Letās imagine your development team just rolled out an update that slightly modified the layout of your application. Instead of spending hours troubleshooting issues caused by small changes, AI software testing helps you easily adjust the test scripts on its own. It allows your team to focus on developing new features and improving user experience, keeping your software agile and responsive to user needs.
Future of AI software testing and test automation
The future of AI in quality assurance and its impact on software testing is incredibly promising. AI is increasingly becoming a game-changer in QA, enabling QA engineers to enhance their software testing processes, efficiency, and accuracy. Here’s a glimpse of what the future might hold:Ā
- AI-Driven Test Automation: AI is revolutionising test automation by enabling the creation of smarter, self-healing test scripts. AI algorithms can carry out application changes, automatically adjust test scripts, and predict potential failure points, reducing maintenance efforts and increasing the robustness of automated testing.
- Intelligent Test Case Generation: AI can assist in generating test cases by analysing requirements, user stories, and even historical data. This predictive capability helps create comprehensive test scenarios covering various potential issues.Ā
- Predictive Analytics in Defect Prevention: AI-powered analytics can predict potential defects by analysing patterns from historical data, code repositories, and testing outcomes. This proactive approach allows QA engineers to focus on critical areas and mitigate issues before they surface.Ā
- AI-Driven Test Execution and Analysis: AI can aid in the execution and analysis of tests by detecting anomalies, patterns, and correlations within test results. This speeds up the identification of issues and provides deeper insights into the root causes of problems.Ā
- Intelligent Test Environments and Data Management: AI can optimise test environments by analysing usage patterns and dynamically provisioning resources as needed. It can also assist in generating and managing test data, ensuring that test environments simulate real-world scenarios accurately.Ā
- AI-Enabled Defect Reporting: AI-powered tools can automatically create detailed bug reports from recorded videos. This means extracting key info like problem descriptions, steps taken, expected outcomes, and other data collected during the recording. It aims to improve bug reporting by adding more helpful details and smoothing the fixing process.
- AI-Powered Continuous Testing: Integrating AI into continuous testing pipelines can enable real-time analysis of software performance, security, and user experience. This facilitates quicker feedback loops and ensures faster delivery of high-quality software.Ā
Artificial intelligence for QA engineers will offer tools that enhance their capabilities, allowing them to focus more on critical thinking, exploratory testing, and strategic decision-making rather than repetitive, manual tasks.
And this future is in fact, already here. How? Bringing aqua into the conversation – an AI-powered software test management system already has the features mentioned above. With aqua, you can automate most of your testing process, especially test case creation and requirements management. You can create entire test cases with a few words and clicks, and say a word to complete a requirement. You’ll be able to utilise AI to polish your existing items and artefacts, generate test data, get suggestions and much more. With Capture integration, youāll also be able to record all your steps throughout the process seamlessly and have 100% visual and informative bug reports. The future of AI in software testing is at your fingertips, will you use it?
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Instead of a conclusion
It is probably too obvious to write a conclusion about the usability of AI for QA engineers. Instead of this, we decided to ask AI itself what are the benefits of incorporating it into your work process. And, telling the truth, we were pretty surprised. This is what AI said:
“You’ll have more time to spend with friends and family. No longer will your work days be filled with endless document reviews and bug reports ā now they’ll be filled with a happy hour with friends, spending quality time with loved ones, and whatever else makes you happy!
You can sleep at night knowing that your job is secure. As long as humans on this planet want things done right (and done right now), there will always be a place for QA engineers .”
Are you sure that AI doesn’t have a soul?!
