In the digital world, innovation is the buzz word and change is the only constant! The software world has indeed taken giant strides and in its wake, software testing has seen a sea change over the past couple of decades. Time is a luxury that software testers just don’t have – whether it is getting the app to the market or updating the frequent changes that are spurred by innovation, competition and the need for constantly improving user experience. This is more so for mobile app testers, with the growing use of mobile apps for every conceivable need.
Manual Testing stood its ground for some years but in due course it could not support the digital world’s fast paced demands of speed and accuracy. This made way for Automated Testing using open-source test automation platforms, such as Selenium, Appium, and other code-based scripting solutions. However, with the increasing frequency of software updates and crunched testing time, testers were finding it increasingly difficult to cope. This posed a great challenge for the software testing industry because apps that could not keep pace lost out to competition. It was this challenge that paved the way for Test Automation driven by Artificial Intelligence (AI) and Machine Learning (ML).
Conventional test automation came with issues like the need for high levels of programming skill, the lack of qualified personnel, difficulties in preparing test data and environments, choice of right frameworks to integrate the development and test environments, integration issues with other tools, problems related to processes and methodology and other such concerns. This was the background that set the ball rolling in favor of AI and ML based automated software testing.
So let’s look at what AI and ML mean and what they do. In simple terms, AI-driven automated testing is a software testing technique in which AI and ML algorithms are used to effectively test a software product, by employing logical reasoning and problem-solving methods, to improve the overall testing process. ML is a field of computer science that uses statistical techniques to give computer systems the ability to ‘learn’ with data, without being explicitly programmed.
The key characteristics of AI and ML based testing systems, are their self-testing and self-healing features. The role of the self-testing feature is to run regular scans, report the outcomes, suggest solutions and update the development team. The self-healing feature makes it possible for the system to automatically make the changes required to rectify errors. Thus the role of AI and ML in automated testing is to bring speed, accuracy, resilience and stability to the software testing process without compromising on the quality of testing. This is what makes AI and ML based systems the hands down choice in the world of automated software testing today.
Here’s a word of caution however: There are still some tests that are better done with Manual Testing like Exploratory, Usability and Ad-hoc Testing – where human observation, intuition and insights help enhance the visual aspects of User Interface (UI). However, AI has started making inroads into visual validity to some extent.
With this understanding of AI and ML in Test Automation, let’s explore the role of AI and ML based Automated Software Testing and how it contributes to the testing process.
Role and Benefits of AI and ML in Test Automation
- Enhanced Accuracy
AI and ML driven testing greatly reduces errors that often occur in repetitive tests, where human fatigue, negligence and boredom can result in frequent and costly errors. The algorithms that drive AI and ML based testing, greatly contribute to test accuracy.
- Time and Money Saver
As seen earlier, the frequent changes and updates in software necessitates a series of changes and this can be very time consuming in a code based system. AI and ML immensely cut down the testing time, as repetitive tasks are identified, recorded and modified automatically by AI driven testing systems, eliminating the need for human intervention in most cases. AI speed is far greater than human speed and this brings benefits not just in time but consequently also in money and efforts.
- Improves Productivity
With AI and ML taking repetitive tasks off the plate of testers, it helps them work smart rather than work hard on doing the same routine grind over and over again. Testers can now use their time far more productively, working on complex aspects of the project where their skills can be fully utilized. This will enhance not just the testers’ productivity but boost the productivity of the entire SDLC.
- Faster Go-to-market Time
AI and Ml automated testing fully supports Continuous Testing, which ensures that development and testing are simultaneously done. This enables bugs to be detected early in the SDLC and in turns greatly compresses the go-to-market time. AI and Ml also enhances Regression Testing which ensures that updates and changes do not negatively affect any of the existing app features, thus affording early market delivery of all changes.
- Increases Test Coverage
The self-testing and self-healing features of AI and ML driven testing, ensure automatic scanning, testing and checking of file contents, data tables, memories, and internal programs which in turn warrant better test coverage.
- Visual Validation
AI with its pattern and image identification abilities has been making strides into testing of visual aspects of apps. Its ability to detect UI controls irrespective of size and shape and analyze them at pixel level – helps identify visual bugs and ensure that the app’s visual aspects are functioning as expected.
- User Friendly
With the high level of competition among AI driven testing tools, there is a race to make these tools increasingly user friendly. As a result even those without much technical or programming skills, can easily use and benefit from AI driven testing tools.
Having reviewed the role and benefits of AI and ML based automated software testing, let’s also keep in mind that AI and ML systems do not replace humans but eases their tasks and improves their productivity. It’s also important to note that though initial investment in these systems puts a hole in the short term pocket, yet in the long run, AI and ML driven automated testing is definitely more cost effective, because it cuts off the embarrassment, time, money and efforts of avoidable rework.
Another important consideration is that for one’s own survival, it’s vital to keep abreast of rapidly changing technology and ensure that the gap is constantly bridged. The sad truth is that it only takes a few years for digital dinosaurs to become extinct – unlike the old world real life dinosaurs that had the luxury of millions of years to adapt before they vanished from the face of the earth!
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