4 key QA activities to solve test automation challenges via AI and ML
Some years ago, AI and ML were privileged and mainly used within tech giants. Time goes by, and now, these innovations entail businesses of any scale, including start-ups, across multiple industries.
Implementing AI- and ML-based technologies provides fast decision-making and better optimization of problem-solving processes. According to the Digital Transformation Trends for 2021 report, 80% of surveyed businesses have proved that AI and ML boost productivity while making companies deliver products faster and increasing ROI.
Within a new era of automation, AI and ML are seen as inevitable parts of software development that strengthen competitive advantage and assist in achieving more accurate results. The same happens with the QA function, test automation included.
However, these technologies are not so easy to implement. Gartner’s research shows that only 53% of projects that introduce AI are successful. In the article, let’s discover the activities for smoothly introducing AI and ML in test automation processes and learn the advantages they bring to the table.
AI- and ML-empowered test automation. Which side are you on?
According to Statista, only the AI market will reach $126 billion in 2025.
Source: Statista
Not that surprising, bearing in mind AI and ML help reach new heights while improving the quality of IT products, speeding up the testing process, and enhancing the overall performance and…
Ensure faster time to market
While traditional test automation helps deliver software faster, introducing AI and ML to the process allows launching IT solutions even more rapidly within ever-evolving software development methods and constantly increasing end-user demands.
Reduce the number of discrepancies
By implementing smart decision-making and automated controlling of processes, companies decrease people intervention, thus eliminating the human factor. Moreover, this frees up QA efforts while allowing specialists to focus on the core objectives.
Improve flexibility
Within continuously emerging changes in software development approaches, QA processes, customers’ needs, etc., AI- and ML-enabled technologies are more adaptable than human beings. Innovations adjust all the necessary components and workflows automatically in a matter of seconds.
Increase the overall test coverage
Automated test coverage detection allows writing much more scripts in an hour as well as running them and getting results — all with the optimized scope.
Reinforce codeless automation
Manually writing scripts is time- and labor-consuming, while AI and ML simplify traditional automated testing within the ability to perform codeless checks. Therefore, smart test automation tools assess QA risks, update test cases, detect issues, prioritize tasks, and many more.
AI, ML, are you there to help test automation?
According to the World Quality Report 2021-22, last year, companies benefited more from implementing automated testing. Two-thirds of respondents (63–69%) perceived better control and transparency of their testing activities as well as increased ROI. But they want more.
Activity #1. Introduce smart test scripts writing
While interacting with the software, AI collects data, makes screenshots, tests the load, and so on. These steps undergo repetitions that help ML technologies learn the expected pattern and compare it to the behavior of the program. When it detects some deviations, the ML algorithms mark it as a potential error. After this, QA specialists manually intervene during the testing process to see if the identified bug is a real problem. The QA experts conduct final verifications to decide what to do next.
Activity #2. Optimize test automation with self-healing AI functions
Self-healing AI tools can easily adapt to changes in the app UI. By running tests, these instruments discover all the elements and occurring activities while recording them and assessing potential QA risks.
When AI- and ML-based algorithms detect some modifications, the tests change automatically. This helps remove threats before they occur while distinguishing whether the program’s behavior is normal or abnormal, and triggering recovery activities when the software has deviations.
Important to mention that AI-based projects provide the whole scope of benefits only in the long-term perspective. As many errors appear for ML, then the more efficient and trusted further performance will be.
Activity #3. Conducting GUI test automation with ML
Testing of the graphic user interface has come to the next level. Now, the old-school process of manually checking UI elements in accordance with mock-ups is automated within ML-powered algorithms.
Image-based testing is becoming the mainstream as ML-powered algorithms recognize different patterns and perform visual verification even on various devices and their configurations.
Given that 81% of consumers are ready to pay more for better UI, there is no room for errors.
Thus, ML-based technologies simplify automated GUI testing and help deliver reliable and user-friendly software.
Activity #4. Automated monitoring
Automation has already reduced human intervention and accelerated testing processes compared to those performed manually. AI- and ML-based technologies speed up workflows even more.
Machines are powerless when performing the tasks that require man’s monitoring and decision-making. To ensure software and hardware operation with no downtimes and pauses for controlling, checking, analyzing, and other human actions, forward-looking companies introduce AI-and ML-enabled algorithms while optimizing routine actions and shortening the delivery time.
Closing remark
Being trending innovations, ML and AI are gaining momentum while helping organizations accelerate time to market and win the competition.
For sure, AI and ML can be used as part of the QA process and will never replace some manual activities or test automation-related checks, anyway for some work, especially mentioned above, AI and ML might be introduced to simplify some QA processes. On the other hand, while introducing such things, it’s important to calculate which efforts on QA will be saved, and which efforts will become extra for other teams. The example is quite simple — the bug submission process. Having in place efforts save for the QA engineer, we should check how much effort will the developer spend to recognize and understand the bug described by AI, check if AI is able to detect duplicated bugs, etc.
With the edge of total optimization of operational and business processes, AI- and ML-powered technologies assist in alleviating test automation workflows and minimize human intervention. Thus, by introducing smart test script writing, ML-based monitoring, self-healing AI functions, and image-based GUI test automation, companies are taking leading positions in the market with error-free and secure software in production.
Reach out to a1qa’s experts to get support on implementing test automation to enhance your software quality.
