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Recently, we kicked off our month-long deep dive into the ways in which artificial intelligence (AI) is impacting the software testing landscape by releasing our brand new Quality Assurance Testing Guide: The Impact of AI on Software Testing.
As we cover in the guide, one of the fundamental test approaches that is set to benefit from the rapid advancement of AI technologies is manual testing; unlocking new opportunities for increased test efficiency, accuracy, and coverage.
More businesses are seeing the value of leveraging AI-enabled tools to accelerate the process of manual testing their software systems. In fact, this market is expected to grow to a valuation of $2,030.75 million by 2033, driven in part by the telecommunications industry. As software products continue to grow more complex, effective testing inevitably becomes more challenging which is why more quality assurance companies are turning to AI solutions to help them work.
In this post, we’ll take a look at some of the ways that AI can be used as part of the manual testing process and explore some of the benefits (as well as challenges) that come with adopting AI as part of a manual test approach.
What is Manual Testing?
Functional testing, black box testing, user acceptance testing, system testing: what do these testing types all have in common? They are each different types of manual testing that can be executed by a human tester.
Manual testing is a fundamental technique for evaluating the quality of a software application and generally involves executing a series of test cases across a selection of devices to identify defects which can be then reported to a developer in the form of a bug report. This process is effective but it can also be very tedious, as a tester must manually perform each test scenario at least once; sometimes multiple times per test device. Depending on the number of test cases per cycle, this can quickly become a time-consuming process.
One of the promises of applying artificial intelligence to a manual testing workflow is that it can accelerate efficiency by significantly reducing the amount of time and manual effort required to execute testing. Not only does this extend to test case execution and analysis but even the process of developing the test cases themselves.
How Does AI-Enhanced Manual Testing Differ from Test Automation?
While using AI within manual testing does offer the capability to ‘automate’ software testing tasks just as test automation does, there are some differences that should be considered.
The primary difference is that automated tests must be pre-planned and defined prior to execution. This means that for every task within a test scenario, a QA engineer must analyze the project requirements and manually author each automated test prior to execution. For projects with a larger scope or that undergo continuous testing, this once again becomes a problem of a significant manual effort being required.
In our new AI testing guide, we cover more of the differences between AI-Enhanced Manual Testing and Test Automation but, for now, know that its application for manual testing goes further than just test execution.
How Can AI Be Used for Manual Testing?
Within software testing, artificial intelligence technologies are applied with the intention of increasing test efficiency and effectiveness. In fact, when using AI, many of the manual functions of the testing process can be automated. This is not isolated to the act of testing itself but also when it comes to developing test cases, making decisions on what features to test, or even generating test data to use.
This diverse application of AI to software testing can be seen in Katalon’s 2024 State of Quality Report, where they found that respondents used AI for a broad range of testing applications. Some of these included test case generation for manual testing (50%) and automated testing (37%), test data generation (36%), test optimization and prioritization (27%), automated bug detection (24%) and correction (17%), and even test planning and scheduling (15%). This demonstrates just how diverse the use cases for AI are and how they go beyond just executing test cases automatically.
The Benefits of Using AI for Manual Testing
Automating Monotonous & Labor Intensive Tasks
AI is particularly effective at automating the manual testing process for routine and labor intensive tasks like regression tests. This frees up testers to focus their time instead on testing more complex scenarios and performing exploratory testing that humans are much better at.
Increasing Test Coverage
By freeing up manual tester resources and automating test case generation, AI can help increase the scope of test coverage — without the added cost.
Decrease Costs
The more of the manual testing process that can be executed using artificial intelligence, the less a company will need to spend on manual testing resources. Human testers will still be needed in order to interpret the results and check for errors but for continuous testing, an AI testing model can be improved and iterated upon each cycle to eventually require less manual execution resources.
Faster Test Cycles
Through automation of the more time-consuming steps in the manual testing process, AI can help reduce the amount of time it takes to complete a test cycle. Automated testing also has the advantage of being able to run more than one test scenario simultaneously as well as be performed during off-hours or weekends, saving valuable time.
The Challenges of Using AI for Manual Testing
While the potential of AI’s application to software testing might sound promising, there are also some distinct challenges associated with incorporating AI into an existing testing workflow.
For starters, AI models usually require a significant amount of high-quality input test data in order to be able to perform effectively. If information about the product that is being tested is protected or contains sensitive user information, this could run the risk of leaks or exposure if the model that is being trained with that data is also publicly accessible.
Second, there is a technical barrier that must be overcome. AI testing tools function differently than those that your team might be used to and don’t always feature the ability to customize their configuration. This might lead to inaccurate results or false positives initially while a test engineer is still developing their proficiency with the technology.
There is also the issue of a lack of human oversight. When leveraging AI to perform a task which involves generating a large output, there is potential for the results of that task to contain errors which might have been identified had a human been involved.
Conclusion
As we’ve shared, the process of manual software testing is set to benefit from AI by offloading much of the monotonous and repetitive testing tasks to automated workflows and helping detect product defects before they occur.
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 manual 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!