The global automation testing market size is expected to grow to USD 20.7 billion in 2021 to USD 49.9 billion by 2026, at a Compound Annual Growth Rate (CAGR) of 19.2% during the forecast period.
– Markets and Markets
Growth in the software industry has never been as rapid as it is today! As organizations understand that “digital-first” is the way forward, the demand for quality software will accelerate in the coming years. Continuous innovation and development in the IT industry have led to the emergence of new test automation techniques that improve the quality of software testing and ensure the rapid delivery of flawless applications for an enhanced user experience.
After analyzing the latest prioritization on technologies and studying the market trends carefully, we have come up with 4 major test automation trends that will change software testing practices this year. Let’s read about these trends and why they are so essential.
#1: Focus on AI and ML-based testing
The worldwide artificial intelligence (AI) software revenue is forecasted to reach a total of $62.5 billion in 2022, an increase of 21.3% from 2021.
Considering the growing popularity and acceptance of AI and ML in almost every segment of business, organizations have now started leveraging AI/ML in enhancing their QA processes. According to Diego Lo Giudice, Vice President and Principal Analyst at Forrester Research, one of last year’s predictions that’s happening now is the increased use of AI and ML in testing tools to make test automation easier. And we believe that this trend will significantly improve software testing practices just as it has transformed the world of application development. Consequently, there will be an overwhelming demand for quality AI/ML-based test automation solutions.
What are the use cases of AI in Test Automation?
- Generating test cases and test case data that is specific to a module.
- Identifying any leaks or defects in testing.
- Predicting the test coverage even before running the test cases.
- Increasing resiliency of testing assets.
- Decreasing the test maintenance efforts significantly.
What are the use cases of ML in Test Automation?
- ML techniques can be applied to determine the scope of test automation.
#2: API- based automation testing
IoT (Internet of things) devices have been around for many years now. According to a recent Statista statistic, by 2030 around 50 billion of these IoT devices will be in use around the world, creating a massive web of interconnected devices spanning everything from smartphones to kitchen appliances. As the demand for IoT devices and wearables soars – from smart thermostats, Alexa, to smart light bulbs and TVs – it is very important to rigorously test these devices. A majority of information shared on IoT devices is personal and sensitive in nature and therefore it is crucial to protect the data before propagating it through internet channels. The only way to identify the risks with IoT devices is to test them using API and not browser-based tools. Consequently, 2022 will see a huge demand for API-based testing.
#3: QAOps in DevOps
QA and software test managers are often under pressure to speed the release of the software. At times, to meet the deadlines, they might accelerate the testing process and inadvertently validate software that may actually represent a negative value proposition to production. Such slipups can harm the business substantially in the long run. This has led to the evolution of QAOps.
As the name suggests, QAOps refers to QA (Quality Assurance) + DevOps (Development + Operations)
QAOps ensures rapid speed and the best quality. It’s a new-age practice that has been around in the industry for the past few years. However, its demand will soar in 2022, as teams are pressed to release high-quality software within the shortest turnaround time. QAOps will enable organizations to integrate
Quality Assurance (QA) into the Software Development Life Cycle (SDLC) from the very beginning. Leveraging QAOps, the QA team in an organization can advise and analyze future development cycles, identify bugs sooner, fix them whenever they come, and accelerate the delivery without compromising the quality.
#4: NLP-based automation tools are under consideration
What could be better than writing test scripts in natural human language, which can be easily interpreted by machines? Yes, scriptless test automation using NLP (Natural Language Processing) is like a dream come true for testers and QA. It enables testers to create test scripts in natural language without any limitations or complexities of traditional test automation. NLP-based test automation can also be leveraged to strengthen model-based testing and can convert simple English sentences/texts to test scripts. NLP-based test automation solutions will simplify and democratize testing. With a short learning curve, it will enable the project managers to contribute actively to QA and test automation.
According to The World Quality Report, “Options such as self-healing capabilities are going to increase gradually, and there is no doubt that these are the future of automation. While the promises are big, we understand these automation tools aren’t yet sufficiently mature.”
As we head into 2022, the growth story of test automation will continue. Therefore, it will be crucial for organizations to consider the trends mentioned above while staying cautious as they review their test automation tools and capabilities before investing their time and money in something new.
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