Predictive analytics as a concept has been widely applied across industries and businesses to derive the required inferences and take informed business decisions. Traditional software quality assurance (QA) is shifting gears and taking on new responsibilities. Hence, there is an increasing need for teams to take an analytics-based approach towards next-generation QA. Organizations need to achieve both quality and speed, which intensifies the pressure on development teams to foresee the kind of challenges and failures that might come up.
One of the greatest highlights of implementing analytics in QA is its capability to predict the future failures in view of the past data sources. Predictive analytics helps extract project or business-critical information from data sets by implementing statistical algorithms and machine learning. This helps generate patterns and estimate future trends that are useful for identifying failure points. This kind of forecast and data is very much required in QA for making proactive decisions.
Predictive analytics implements multiple algorithms to process the data, namely, regression algorithms, time series analysis, and machine learning. Quality assurance and testing has been a complex activity and involves many dependent variables. It needs to be efficiently managed to deliver the expected results. Analytics can be effectively leveraged to streamline and smoothly perform software testing activities.
Moreover, it is not a one-time activity, as it has to be continuously conducted to analyze the data that is constantly generated during the software development process. When the stored data is analyzed with analytic solutions and tools, it will continue to add business value towards the end of the development process. The process needs a good amount of data churned from software development cycle to deliver these results efficiently.
Digital transformation is changing the business dynamics, where quality assurance plays a major role to deliver strong solutions for dealing with the customer base. For competing organizations, there is very less scope for error. Analytics can help support teams in the testing process to not only bring down the testing costs but also cut down the testing efforts. Ultimately, help businesses to reach faster to the market and cut the chase.
The need to reach faster to the market and stay accurate as much as possible are two of the most critical reasons for considering predictive analytics in QA. Let's evaluate some key reasons to adopt analytics in the QA and testing space.
It is important to understand the overall market scenario and consumer sentiment to develop the right applications for the consumers. Analytics applied in QA helps gauge the consumer sentiment on product and applications.
