Quality at Speed
The exponential and unprecedented change in technology affects the way in which the organizations develop, validate, deliver, and operate the software.
Hence, these organizations must consistently innovate and revamp themselves through finding the solution to optimize practices and tools to develop and deliver high-quality software quickly.
Accounting for roughly 30% of the total project effort, software testing is an important focus for changes and improvements. Testing practices and tools need to evolve to address the challenges of achieving “Quality at Speed” amid the increasing complexity of systems, environments, and data.
Let us see the new trends in testing which helps in the challenges of achieving “Quality at Speed”
Artificial Intelligence for Testing
Although applying the artificial intelligence and machine learning (AI/ML) approaches to address the challenges in software testing is not new in the software research community, the recent advancements in AI/ML with a large amount of data available pose new opportunities to apply AI/ML in testing.
However, the application of AI/ML in testing is still in the early stages. Organizations will find ways to optimize their testing practices in AI/ML.
AI/ML algorithms are developed to generate better test cases, test scripts, test data, and reports. Predictive models would help to make decisions about where, what, and when to test. Smart analytics and visualization support the teams to detect faults, to understand test coverage, areas of high risk, etc.
We hope to see more applications of AI/ML in addressing problems such as quality prediction, test case prioritization, fault classification and assignment in the upcoming years.
Mobile Test Automation
The trend of mobile app development continues to grow as mobile devices are increasingly more capable.
To fully support DevOps, mobile test automation must be a part of DevOps toolchains. However, the current utilization of mobile test automation is very low, partly due to the lack of methods and tools.
The trend of automated testing for mobile app continues to increase. This trend is driven by the need to shorten time-to-market and more advanced methods and tools for mobile test automation.
The integration between cloud-based mobile device labs like Kobiton and test automation tools like Katalon may help in bringing mobile automation to the next level.
Test Environments and Data
The rapid growth of the Internet of Things (IoT) (see top IoT devices here) means more software systems are operating in numerous different environments. This places a challenge for the testing teams to ensure the right level of test coverage. Indeed, the lack of test environments and data is a top challenge when applying to test in agile projects.
We will see growth in offering and using cloud-based and containerized test environments. The application of AI/ML to generate test data and the growth of data projects are some solutions for the lack of test data.
Integration of Tools and Activities
It is hard to use any testing tool that is not integrated with the other tools for application lifecycle management. Software teams need to integrate the tools used for all development phases and activities so that multi-source data can be gathered to apply AI/ML approaches effectively.
Organizations and individuals need to remain aware of the developments in the industry. Keeping up with these trends would give test professionals, organizations, and teams the opportunity to stay ahead of the curve.
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