In the quickly changing world of software testing, testers everywhere face many challenges. In this blog post, we’ll take a look at the current testing situation, talk about common problems testers deal with, and see the role of AI in automated testing.
Understanding the Testing Landscape
Testing began as a manual process, but with new technology, automated testing became the norm. However, the failure rate in test automation remains high, hovering around 80%. The Test Automation Wall, as we’ll call it, poses significant obstacles. The primary issues include the time-consuming test creation process, the need for specific skills, and the increasing complexity of covering diverse platforms and systems.
The main culprit behind many organizations’ failures in test automation is maintenance. If tests cannot be easily maintained, failure is inevitable. As the number of tests grows, the struggles intensify, leading to an eventual breaking point. To succeed in this journey, a smarter approach is needed—better test design, improved automation, and user-friendly tools and frameworks.
The Role of Artificial Intelligence
AI emerges as a key solution to overcome the Test Automation Wall. It facilitates easier test creation, efficient maintenance, and broader coverage across multiple platforms and operating systems. AI’s ability to adapt to different technologies and its independence from unique identifiers like HTML IDs make it a powerful ally in the testing domain.
What is AI?
If there are still people unfamiliar with AI, AI is a subfield of computer science emphasizing the creation of intelligent machines and software. AI comprises four main tools: Artificial Intelligence, Machine Learning, Deep Learning, and Natural Language Processing (NLP). These tools collectively enable machines to mimic human thinking and interaction.
AI in Test Automation
AI in test automation revolutionizes the traditional approach. Unlike traditional tools that require knowledge of underlying technologies, AI doesn’t care about the technical details. Machine Learning and Deep Learning adapt to various scenarios by learning from data, while NLP facilitates human-like communication with computers.
Implementing AI in test automation brings substantial benefits, including a 70% reduction in maintenance costs, a 30% or more increase in automation coverage, and a threefold acceleration in test development. AI enables a shift left approach, allowing testers to start creating scripts even during the design phase using mockup images.
AI Integration with Selenium and Playwright
AI Integration with popular automation tools such as Selenium and Playwright works seamlessly. Open-source libraries and the possibility of creating custom models provide flexibility for testers to choose the best integration method for their environment.
Training AI for Testing
Training AI for testing is worthwhile as it significantly reduces the time spent on script creation and maintenance. Once a model is in place, one script can be used across various platforms, saving time and effort in managing multiple test cases.
Choosing an AI Automation Tool
When it comes to AI automation tools, there isn’t a one-size-fits-all solution. The choice may depend on various factors, including project requirements and budget constraints. One notable tool mentioned is OpenText UFT One, which has been evolving for four years and is technologically independent, providing flexibility for users.
Conclusion
In conclusion, as in all areas, AI is making big changes in test automation, offering a smarter and more efficient way to tackle the challenges posed by the Test Automation Wall. Testers are encouraged to embrace this technology, keeping an open mind and exploring its potential for their specific testing needs. As AI in automated testing continues to integrate rapidly into our lives, staying informed and adaptable is key for testers to thrive in this ever-evolving field.
If you still haven’t, read our post on regression testing and deepen your knowledge in this area.
