- Introduction to AI in QA and test automation
- Benefits of AI in software testing
- Popular AI-powered tools for test automation
AI in test automation — It’s more than a buzzword
AI in testing is becoming mainstream. According to Statista, the global AI software market is expected to reach around 126 billion U.S. dollars by 2025. That’s astounding, isn’t it? AI has impressively made its way across every domain including QA. QA aims to transcend customers’ expectations by delivering quality products that are error-free. AI can help that happen.
As a matter of fact, the transformative role of AI in QA makes the latter paramount. paramount. There is no doubt that long regression testing cycles, assurance of adequate test coverage, maintenance of automated scripts, defect leakage, and ignored bugs make QA even more difficult. AI not only brings ease to the automation testing process but also helps in taking the quality of the output up a notch. AI-based testing leverages AI and ML algorithms for product testing.
Hence, it would be interesting to understand how it can revolutionize the world of software testing in 2022 and beyond. We, being a leading-edge QA automation services company are at the forefront of AI and would like to share some of the insights with you.
Let’s check what the statistics say,
- The test automation market is projected to rise at a 14.2 percent CAGR during the prediction period from 2021 to 2026. The automated test market is driven by expansion in the demand for automation of the test procedure and solutions to flawless customer experience and the growing demand for AI-centric cutting-edge technologies for QA environments.
- As per IDC’s research, 40 percent of the G2000 will double the usage of intelligent automation in information synthesis, dissemination, and knowledge retention by 2026, filling the expertise space in the data-to-insights lifecycle.
- Verified Market Research revealed that the Intelligent Process Automation Market size was esteemed at $8.52 Billion in 2020 and is expected to hit $21.63 billion by the year 2028, rising at a CAGR of 12.37 percent from 2021 to 2028.
- According to Marketsandmarkets.com, the worldwide test automation market size is anticipated to hit USD 49.9 billion by 2026 from USD 20.7 billion in 2021, at a CAGR of 19.2 percent during the estimated period. The test automation market is rising due to the fast adoption of most modern technologies. Source
Advantages of AI in software testing
- Effectiveness – Time and cost are every organization’s major concerns. AI can automate the test processes and increase the pace of continuous testing. AI helps automate repetitive tasks that result from source code modification with lesser effort and costs. AI provides better test coverage. They can run numerous tests in one go, hence providing more extensive coverage, which is not possible in manual testing.
- Taking quality up a notch – Infusing AI in software testing can improve the quality of the output. AI/ ML tools are capable of reading the changes that are made to the app, hence they can fathom the relationship they have. It offers great test coverage, enhances defect tracing, improves regression tests, and conducts visual testing. AI-based testing improves defect tracing which is a major plus. It quickly outlines these bugs for the software developing team to operate smoothly.
- Self-healing and auto-generation of test scripts – Self-healing scripts detect underlying patterns of change and execute corrective measures at runtime. AI also generate frameworks and test scripts on the basis of historical test data and consumer behavioral pattern. It provides intelligent insights and has the capability to facilitate self-repair which is involved in the implementation of selenium tests.
- Enhances accuracy and efficiency
- Eases test execution
- Eliminates dependencies
- Increases test coverage
- Saves time and money
- Improves regression tests
- Manages defect tracing
- Automates API test generation
- Fastens a product release
- Conducts visual testing
- Automates visual validation
- Eliminates flaky tests
- Boosts writing of test cases
- Helps in faster and more stable UI tests
- Minimizes maintenance
How to use AI in your test automation
- Visual testing
Testing the UI or the visual look of a web page with its baseline image expectation can be considered the major use case of AI in test automation. The AI/ML-powered tools can test the UI across different browser configurations, find anomalies, and bifurcate the findings. AI also creates powerful visual locators that can remove the issues raised by hard-coding with selectors.
- API testing
API testing requires testers to consider a plethora of scenarios and have good test coverage. AI-based API testing tools can be used here to evaluate API workloads in production and test environments, find out the patterns between the REST calls, and group them by scenario. AI algorithms can examine millions of API calls, making a QA engineer’s life hassle-free. AI also helps in managing test inputs, assisting in generating test cases, and seamless reporting and feedback.
Any changes in the source code could break the test cycle. This poses a challenge to QA professionals in re-running the entire test suite that ultimately makes them struggle with CI/CD. that QA professionals struggle with CI/CD is to re-run the entire test suite as any change in the main code can break the test cycle. Apart from this, other challenges include the cost of failure, outages, misconfigurations, etc. AI can dramatically help here. AI frameworks can store data, observe existing tests, and find out patterns. AI-based intelligent and low-code solutions can speed up the pace of development workflows and processes hence improving the speed of software delivery.
AI automation uses ML to write tests for the app by spidering automatically. It is an approach where a test bot browses through a web page or app in a systematic way through links or forms to assist QA professionals in indexing, reverse engineering, automated test case generation, etc. You just need to point the modern AI/ML tools at your web app to start crawling.
AI-powered tools for test automation
- Sauce Labs
- Parasoft SOAtest
What do AI automation testing tools commonly do?
The global AI-enabled testing tools market is expected to project a stellar CAGR of 16.9% from 2022 to 2030, with an estimated market value of around US$ 364.5 Mn as of 2022, source. These tools are known to improve product accuracy. They perform predictive self-healing and intelligent bug hunting. These tools are capable of making apps resilient by applying predictive auto-scaling. And, they can infuse automation into business workflows or processes for end-to-end testing.
Possible challenges in adopting AI-based QA tools
- Lack of skilled resources.
- Inadequate AI/ML process knowledge.
- Issues in integration with existing apps.
- Lack of clarity in exact use case and implementation.
- Availability of structured or unstructured data without any human bias.
The future of AI and ML looks bright. It has the power and potential to reimagine the future of automation testing by offering mature solutions to solve testing problems. New testing roles like AI QA strategists and AI test specialists are also emerging at a tremendous pace to accommodate the increasing demand for AI in testing.
Zymr is a leading QA automation services and test automation company helping global ISVs and enterprises with our AI test automation capabilities. We have helped them move faster at the desired quality. In case, you wish to speak to our QA Automation experts, drop us a message.