A new era of test automation is upon us, and in a recent study nearly 90% of organizations reported that, in their quest for quality, the application of Artificial Intelligence (AI) is now the strongest growth area within their test activities.
Organizations using AI-powered intelligent test automation within their continuous testing pipeline are delivering testing efficiencies in the cloud and on-premises, on any application, service, browser and mobile device . The latest AI-infused functional testing solutions are helping to deliver exceptional experiences, everywhere, with high quality applications.
What challenges prevent us from scaling test automation?
Writing automated tests that run perfectly against one environment is challenging in and of itself, but what about when you’re ready to scale your one suite of tests to run in multiple environments, applications, browsers and devices?
At this point, you may find that different environments have different information such as IP addresses or URL, login credentials, SSL/TLS certificates, database information, and so on.
It’s a good practice to extract this kind of information and place it in specific test artefacts, or properties files. With the use of properties files you can separate out the nuts and bolts of the test environment configuration allowing you to focus on the actual objectives and content of the test.
Planning your development activities and architecture of the automation framework during the early phases of test automation, even if you are starting with just a single environment, will help to minimize the amount of rework that will be needed later on in the project as you scale up your test automation capability.
In comparison to coding multiple conditional paths, managing test data in all other artifacts needed for test automation also becomes more challenging when running across multiple environments.
These challenges mean that we have low test automation rates (typically 30% and it is decreasing) while the maintenance cost is high (50% and increasing).
Using artificial intelligence (AI) in test automation has the potential to revolutionize the way we approach software testing. By leveraging machine learning algorithms, test automation frameworks can learn from past test results and make decisions on which tests to run, as well as how to best execute those tests. This can lead to more efficient and effective testing processes, as well as improved quality and reliability of the software being developed.
One of the key benefits of using AI in test automation is the ability to optimize test execution. Traditional test automation frameworks typically follow a predetermined test plan, which can lead to unnecessary test runs and wasted resources. With AI, however, test automation frameworks can analyze past test results and prioritize the most impactful tests to run first, based on factors such as test coverage and risk level. This can help reduce the overall time and effort required to test a given application.
Another benefit of using AI in test automation is the ability to identify patterns and trends in test results. By analyzing the results of past tests, AI algorithms can identify common issues and failures, as well as their root causes. This can help testers quickly identify and fix problems, leading to faster development cycles and improved software quality.
There are a few key considerations to keep in mind when using AI in test automation. One is the need for accurate and reliable data. AI algorithms rely on data to make decisions and predictions, so it’s important to ensure that the data being used is accurate and up to date. This may involve implementing processes to continuously monitor and validate the data being used.
Another consideration is the potential for biases in AI algorithms. Like humans, AI algorithms can be influenced by biases in the data they are trained on. It’s important to be aware of these biases and take steps to mitigate their impact, such as using diverse and representative data sets for training.
There are also a few technical challenges to consider when using AI in test automation. One is the need for robust and scalable infrastructure to support machine learning algorithms. This may involve investing in additional hardware and software resources to ensure that the AI algorithms can run smoothly and efficiently.
Key Benefits of Using AI in Test Automation
- 3x increase in test development speed
- 30% or more increase in test automation coverage
- 70% or more reduction in maintenance costs
- Extend reach so that everyone can test
Overall, using AI in test automation has the potential to significantly improve the efficiency and effectiveness of the software testing process. By leveraging machine learning algorithms, test automation frameworks can learn from past test results and make intelligent decisions on which tests to run and how to best execute them. While there are challenges to using AI in test automation, the potential benefits make it an exciting and promising area of innovation in the field of software development.
