Q&A with Tal Barmeir, Co-founder & CEO of BlinqIO
- Can you provide an overview of the importance of software testing?
In today’s world, a significant amount of code is generated by AI, meaning software testing is especially important, particularly functional end-to-end testing. One reason for this is that AI algorithms are not infallible. They can generate code that is syntactically correct but doesn’t function as intended or meet the specific requirements of the project. Functional end-to-end testing verifies that the AI-generated code performs the desired functions correctly across various scenarios and use cases.
It is also possible that AI-generated code might introduce unforeseen bugs or stability issues, especially when integrated with existing systems. Functional testing helps ensure that the new code works seamlessly with other components of the system, maintaining overall stability and reliability. Ultimately, the end goal of any software is to satisfy user needs and provide a seamless user experience. Regardless of how code is generated, functional testing is essential to ensure that the software meets user expectations and delivers consistent, error-free performance.
- How has software testing changed over time to meet business demand?
Software testing has evolved significantly in recent years, adapting to technological advancements and the growing complexity of software systems. For example, initially, software testing was predominantly manual, involving human testers executing test cases without the aid of any tools or scripts. They would manually interact with the software interface, providing inputs, and comparing the actual outcomes with expected results. Unfortunately, manual testing was time-consuming, prone to human error, and often repetitive. However, it was crucial for detecting usability issues and understanding the user experience.
To address the limitations of manual testing, test automation emerged, which involves writing scripts to automate the execution of test cases. These scripts perform predefined actions on the software being tested and then report the outcomes. The advantage of test automation is its ability to execute repetitive tasks quickly and consistently, thus saving time and reducing human error. However, it still requires significant human intervention, particularly for writing, updating, and maintaining the test scripts.
The latest advancement in the field is autonomous testing. This extends beyond traditional test automation by using artificial intelligence (AI) and machine learning (ML) to enhance testing processes. Autonomous testing systems can not only execute predefined test cases but can also generate new test cases, predict potential areas of defect, and learn from past test results. They can adapt to changes in the software being tested, reducing the need for manual intervention in maintaining test scripts. This approach aims to make the testing process more efficient and effective, capable of handling the complexities of modern software systems.
- How does AI-powered software testing compare to traditional testing?
Autonomous testing is superior to traditional testing for a number of different reasons. Firstly, it allows for the dynamic and instant scaling of virtual testers. This adaptability is crucial for efficiently managing varying testing workloads. During peak periods, additional virtual testers can be instantly deployed to handle increased demand and then scaled back when not needed, optimising resource usage and reducing costs.
Autonomous testing also involves self-maintaining test scripts. This feature significantly reduces one of the primary bottlenecks in test automation – script maintenance. It can autonomously analyse failed test executions and determine whether the cause is a genuine bug or a required change in the test script due to a UI alteration that doesn’t impact the business logic. This capability ensures that testing remains effective even as user interfaces evolve.
Furthermore, autonomous testing supports testing in approximately 50 languages, enabling comprehensive testing coverage for applications targeting a global audience. Similarly, it can configure and mimic different user behaviours, enhancing the ability to cover diverse user scenarios. This is particularly beneficial for applications with a varied user base, ensuring that the software is tested across a wide range of user interactions and experiences.
Finally, autonomous testing is agnostic to various device platforms, including desktop, web, mobile, and tablets, enabling the software to function properly across all intended devices and platforms. This also ensures that it remains effective regardless of changes or updates to the front-end and provides an adaptability that is crucial in modern software development where front-end technologies can frequently change.
- Can AI-powered software testing improve a company’s profitability?
Autonomous software testing is more efficient and cost-effective compared to traditional methods for several reasons. It automates the entire process of creating and executing test cases, significantly reducing the need for manual input. This reduction in manual labour lowers labour costs and frees up human testers to focus on more complex and creative testing tasks that cannot be automated. Utilising an autonomous software testing system also avoids the risks of human error and enables human testers to concentrate on areas where they add more value, such as exploratory testing, usability testing, and providing insights that require human judgement and experience, all of which will greatly enhance efficiency.
Secondly, since autonomous testing systems can operate continuously without fatigue, they can complete test cycles much faster than human testers, enabling quicker feedback loops in the development process, as well as faster iterations and deployments. As such, autonomous testing can cover more scenarios and edge cases than human testers, resulting in more thorough testing and, ultimately, a higher-quality product. This also enables bugs to be identified and fixed early in the development process which is much more financially effective than addressing them post-release.
Lastly, automated testing addresses one of the biggest testing challenges – maintaining the test scripts. This problem becomes increasingly likely when the software undergoes frequent changes. Autonomous testing systems can update and maintain their test scripts autonomously, greatly reducing the labour and costs associated with script maintenance.