AI & ML in testing — how relevant are these?
Before understanding the relevance of artificial intelligence in quality assurance and testing, it is important to understand the difference between AI and ML. Machine Learning is a subclass of AI, while AI is any software code that makes the computer do smart things, also taking over some tasks from humans that are repetitive and menial. Machine Learning, on the other hand, consists of deep learning techniques that help these robots learn to get smart. The bots learn from human interactions and, in the process, get smart to replace human beings and carry out specified tasks.
For example, robots in RPA automation are usually assigned to back-office tasks in industries like healthcare, banking, etc., that need to be done consistently over time with minimal human intervention. Or, some tasks are high-volume, such as claim processing in the insurance industry, or are time-consuming have AI-ML-powered robots handling the work.
Relevance of AI & ML in testing
One of the important aspects of testing or QA is challenges like change in the Domain object model, selectors change, etc., giving way to shaky and unreliable tests. Artificial Intelligence and Machine Learning help make tests steadfast.
The first step towards incorporating AI and ML into testing is to choose the right model. But, as Peter Norvig, the Director of Research at Google, says that it is not the algorithms at Google that are good, but the volume of data that Google has makes it what it is today. So, in other words, data is of critical importance in any ML ecosystem. So, the steps to follow are to get the right testing model and then get training data to use ML to its maximum.
Detecting an anomaly in testing is a great way to understand key performance parameters like memory and CPU utilization and more. Another feature to look for is to assess device type behavior. In testing, a lot many prediction techniques combine predictive analysis with anomaly detection. It works best because the entire system has a very heterogeneous growth — some systems become complex over time, while some become consistent with each passing day. Using predictive analysis with anomaly detection works effectively as it makes testing and QA processes intelligent. Image comparison is one technique that is revolutionizing the gamut today.
Ten years back, comparing two images was done using pixel matching or template matching techniques. The process used comparing images pixel by pixel. It helped detect issues that the naked human eyes could not. However, the process had severe limitations. However, there have been drastic changes in the last five years with lots of innovation in this niche, with many ML models launched that work on Convolutional neural networks. The result has been a fine-grain analysis between two images. ML and AI help the model learn what items on the two images match and what are different.
To know in-depth about how AI and ML can impact testing and QA, speak to experts at iBoss Tech Solutions software development specialists in the cloud technology gamut.