The demand for productivity, greater reliability and insight

QA and DevOps business are growing increasingly complex over time—due to legacy systems and the evolution into heterogeneous environments such as, software-as-a-service technologies, private clouds, public clouds, mobility, and the numerous integration points in between that keep it all working.

The number of systems that must be tested and managed is increasing at a tremendous rate, and the complexity and volume of resources per release is growing too.

Many organisations still test their systems manually, which is expensive and not terribly effective. Many testers write scripts to test requirements, gaps, chasing change, but this approach gets unwieldly over time as the volume of scripts that need to be managed just keeps growing. Testers usually spend as much as 50% of their time on routine, repetitive tasks such as searching for their data and checking if their data is ‘fit for purpose’.

This creates bottlenecks and obstructs the ability to deliver innovation to the business fast enough to stay ahead of industry peers.

It is estimated that the hourly cost is up to $100k per hour and the cost of critical application failure can be between $500k – $1m per hour.

What if you could avoid common problems in your QA and DevOps such as high-defect rates, missed release timelines, and test coverage gaps?

In order to minimise downtimes and be able to release quality software in the market, teams need to have insight, they need to know when the system has changed, what has changed and who changed it and what steps are required to fix the issues. As software development and testing teams move from waterfall approaches to continuous delivery and continuous testing, QA and DevOps needs monitoring that brings greater visibility across the entire lifecycle, and unifies people, processes, and tools.

AI and Analytics at the core

Automation can reduce costs, save time and increase ROI, but it needs to be implemented intelligently to achieve the best business outcomes.

By embedding AI and Analytics in your testing process, it helps to identify issues faster and focus your testing efforts on those parts that have the biggest impact on the user experience. This means to provide better information back to developers to reduce the time-to-fix, recommend what tests to perform and predict the business impact of a new release.

But This is just the tip of the iceberg.

It is a common secret, that the cost of a defect rises significantly the later in the process it is discovered. So Identifying and resolving a defect as early as possible, improves the quality of your release and reduces the cost and time to market. It is even better when defects can be prevented in the first place. The more defects you prevent, the lower the risk is of defects slipping through your testing procedures and making their way to the end-user.

Using AI and analytics in the testing process, enables to find bugs in the software and alert the developers how, when, and where these bugs entered the system, rather than just send them failure notifications. It also enables them to predict where defects may occur based on defect patterns and historical trends. Doing the same with manual testing takes a lot of time, and slows down the development and testing process. Various tests can be performed around the clock and at every stage to ensure the reliability and usability of a system.

The financial services sector is a great fit for intelligent automation as they struggle to keep pace with new product introductions and increasing digital demands, as it will enable them resolve typical testing challenges and inefficiencies.