How about predicting the possible problems in your product way before they occur in real? Ever wanted to optimize the process at every stage of development so that the delivery delays can be reduced? We know, QA projects are inherently unpredictable. Complex development process involves cost overruns and schedule slips.
Why?
Because during the initial phase, you have got a little idea about how long a project will take, what it will cost, or what they’ll finally be able to deliver to the end customer. No wonder initial project plans are more of calculated guesswork (Makes sense as you do not have the entire plan in place!).
A recent study by Mckinsey shows, among 1800 completed software projects, only 30 percent of them met their original delivery deadline. More than 50% of the projects suffer overruns, most of which involve potential bug fixing at the end of the development cycle.
Here comes the Predictive Analytics and Agile testing model.
But first, why Agile?
Because it facilitates Continuous Iterations Continuous Development (CICD) based on the data (specifically predictive data). The process helps in identifying failure points well in advance (based on the trends as well as past failures) and works on it throughout the development cycle. The idea is to scale up the process and reduce time to market.
How Predictive Analytics is Addressing the Challenges of Traditional QA
Unlike traditional testing, agile models involve continuous iterations based on the predictive data available from studying past trends. Dealing with failure points at the end of the development cycle results in unnecessary delays. By implementing machine learning and statistical algorithms, predictive analytics extract information for past data sets to generate patterns. It helps in creating an information database (otherwise impossible in traditional QA models) that estimates future trends and identifying failure points well in advance.
Customer-centric QA
The main focus is on technical and business requirements when it comes to traditional testing; customers’ usage patterns are totally ignored. On the contrary, applying analytics helps developers to assess the emotions of the customer over the product and applications. Listening to the customer feedback on the beta version helps the team to deal with compatibility issues, performance issues, functional issues that can be addressed with a consumer-centric viewpoint.
Creating Roadmaps for Future Testing Models
Traditional waterfall testing strategy does not involve real-time learning as there is no feedback loop. However, the scenario is exactly the opposite of the agile testing models. Every time a test is run, you create log files, log defects compiling reports, which helps in learning more about user experience. The testing team can align test scenarios and identify critical issue patterns to ensure adequate coverage every time they develop a product. It revamps the development workflow and simultaneously identifies the possible mishap spots with the previously known data points.
Enhance Test Efficiency
With real-time user inputs, the QA team gets to know more about user demands. Predictive data enables the QA team to reach out to the root cause of the failures, thus forecasting on the possible defect ranges as well as the risk of modules for future versions. The idea is to perform continuous analysis and evaluation and address the failure points at every stage of the development cycle. It reduces last-minute glitches as well as the time to market.
The Final Takeaway
“Predictive Analytics is making a big time impact on the agile testing models like everything else in the technology domain. Testing in an agile environment has been a challenge. To keep up with the demands of faster release cycles, more than 99% organizations consume a lot of time and resources. But, ultimately it delays delivery. With predictive analytics in place, you get a realistic insight to the release schedule, and also get to know if there’s any reason that is making the project lagging behind”, says Abhishek Rungta, the CEO of INT.
