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This month, we’re doing a spotlight on the ways in which AI technologies are helping software testing teams accelerate their work and changing the role that human testers have in the software testing process.
As part of this, we published our new, comprehensive guide Quality Assurance Testing Guide: The Impact of AI on Software Testing as well as a deep-dive blog into How AI Can Be Used for Manual Software Testing.
AI technologies excel at being applied to tasks where automation is most appropriate — which makes them a perfect candidate for the use in Test Automation. But what advantages does using AI offer for a testing discipline that is already based around conducting automated tests and reducing the amount of human intervention? And how can it be applied to existing test automation workflows to increase efficiency and reliability?
In this post, we’ll take a look at some of the ways that AI can be used as part of the automated testing process and explore some of the benefits (as well as challenges) that come with integrating AI technologies into automated workflows.
What is Test Automation?
Test Automation is the process of automatically executing testing using software tools, code scripts, and other technology frameworks. Across the QA industry, Test Automation is surging in popularity due to its ability to automate repetitive tasks and increase the speed at which testing can be completed.
Test Automation does have its limitations; namely in that it is not always possible to automate every scenario, based on either its complexity or the type of testing that is being conducted. There’s also a large, initial cost associated with setting up test automation such as obtaining software licenses, training personnel, purchasing hardware, and more. Couple that with maintenance costs and the need to still have human operators available to develop tests and validate results and it becomes challenging to commit to shifting entirely to an automated workflow.
So where does AI fit into the Test Automation process? And what benefits does it add to a workflow that is already built around automating testing?
How is AI Changing Test Automation?
Artificial intelligence offers benefits for Test Automation that make it worth considering as a workflow addition. Through the application of machine learning, an AI is able to learn from the tasks it is completing and improve the way it functions. This is especially helpful for test automation since the types of testing and scenarios it’s being applied to are generally run quite often and therefore have plenty of data to use for training.
Another advantage is that as an AI is learning from automated test execution, it is also able to help predict and, in some cases, prevent defects before they occur. By analyzing data from previously executed tests, as well as code changes, an AI can evaluate the potential impact of an update and provide insight into what features it might affect. That way, a test team is able to save time by avoiding complete test suite updates with each new feature release and only updating the areas which are anticipated to be affected.
How Can AI Be Used to Automate Testing?
Let’s explore some of the ways that AI can be applied to Test Automation to enhance the way it functions or unlock new capabilities that were previously not possible.
Automated Test Case Generation
Utilizing generative AI and a collection of quality input data, it is possible to develop test scenarios that cover not only the functional requirements of an application but also any potential code vulnerabilities or usage patterns which should be considered for testing. It is important to note that while these provide an excellent starting point for a test engineer to develop a test script from, these are not directly executable without manual human testers involvement first.
Test Data Generation
Arguably one of the most challenging parts of software testing is having access to good test data that can be used to accurately simulate real-world scenarios. Thankfully, AI is quite useful for this, as it is able to leverage machine learning to analyze real-life input test data and produce an output which is characteristically similar. This can then be used as part of testing; eliminating the risk of exposure of sensitive information while also having the flexibility to be regenerated, as needed.
Self-Healing Tests
In traditional test automation, a common pain point is when elements of a software product change over the course of the test cycle, leaving test scripts which once functioned without error now requiring maintenance from a test engineer before they can be run again. With the application of AI, automated tests instead have the capability to ‘self-heal’ mid-execution or recognize elements that have changed since the last successful run and, instead of failing, relocate it and resume execution.
Test Suite Optimization
Over time, it is inevitable that some tests within a suite will become either redundant or ineffective and need to be optimized or removed entirely to make sure that only the most effective tests are prioritized. AI can help achieve this by analyzing data from previous tests and identifying which tests are inconsistent, underperforming or unused.
The Benefits of Using AI to Automate Testing
AI’s application to test automation offers some key benefits when compared to traditional automated test approaches.
Reduce Testing Time
AI-powered technologies can help reduce the amount of time required for testing by identifying the most critical areas that should be prioritized and automating time-consuming tasks like regression or smoke tests.
Reduce Manual Effort
As more of the monotonous and laborious testing tasks become automated, the amount of manual effort required each test cycle decreases. This reduces the burden on testers and allows an organization to utilize its testing resources in other areas instead.
Improved Test Coverage
By freeing up manual tester resources and automating test case generation, AI can help increase the scope of automated test coverage — without the added cost.
The Challenges of Using AI to Automate Testing
Leveraging AI for test automation doesn’t come without its challenges, and how capable a team is at mitigating each will determine how successful they will be at integrating AI into an existing automated workflow. Here are just a few examples of challenges you may face:
Data Quality
Without access to quality data for training, an AI model runs the risk of producing inconsistent or ineffective results that would affect the outcome of testing. To circumvent this, a team should seek to develop a comprehensive and diverse set of training data that represents the full scope of the software product being tested before implementing an AI solution.
Technical Complexity
AI testing tools inherently come with technical challenges that must be overcome during the implementation and configuration process. Since they might function differently than tools your team is used to, this can lead to inaccurate results or false positives initially while they develop their proficiency with the technology. There’s also a risk of compatibility issues when attempting to integrate AI into an existing workflow. For these reasons, it is generally recommended that a team uses a smaller project as a starting point and then progressively increase their AI testing capabilities as they develop experience.
Algorithmic Biases
Depending on the quality of the training data, AI tools can exhibit biases in their machine learning model that may cause it to ignore critical defects or provide unreliable results. Ultimately, your success with addressing bias will depend on how comprehensive your training data is and the frequency at which it is updated (as the software product evolves.)
Transparency in Models
AI testing tools can provide remarkable results when implemented correctly, but without some level of transparency into how each decision is made or the inner workings of a model, it can be difficult to build trust in its efficacy. AI models which produce explainable results make it easier for project stakeholders to understand the decision-making process and become more confident in the model’s output.
Conclusion
As we’ve covered, AI technologies are already having a tremendous impact on the way that test automation functions and introducing new capabilities that streamline the testing process. Teams which choose to leverage AI testing tools as part of their workflow will see clear benefits for efficiency, accuracy, and overall test coverage; so long as they’re willing to meet the challenges head on.
Even in an automated future, there still remains a need for quality, human testers to integrate AI technologies, monitor results, and test from the perspective that only a human can provide. There is no denying many of the efficiency gains that come from incorporating AI into the testing process but without human testers, it can only go so far.
If you’d like to learn more about automated testing with AI or how PLUS QA can support your product launch with real device testing and a team of quality assurance experts, get in touch with us today!