Reading Time : 0 Mins

DevOps And Test Teams Burnout: Fixes And Releases

Lead Marketing Strategist

An INFJ personality wielding brevity in speech and writing.

According to Gartner, 95 percent of applications running in production today are not instrumented.

Shipping defect-free software which has been used by millions of people requires extensive testing to ensure stability and performance. Despite Continuous Testing and Continuous Integration, defects seem to be inevitable.

When the issues arise in the production environment, all the could/should-haves will also step in and after a long meeting, Engineers will go back to fixing the issue and Test team to test them again and give a go for deployment.

There is a 90% chance for the patch to fail again if the QAs don’t define test case scenarios rightly. QAs with their existing domain knowledge should be able to do the justice of testing the right test cases and guarantee the success of patch.

These traditional practices are purely subjective, and it’s commonly known as risk-based testing. Maximum test coverage is not completely possible in the current form of risk-based testing and it poses a series of challenges to the deployment.

How Machine Intelligence can augment Quality Assurance

The World Quality Report states “the most important solution to overcome increasing QA and Testing Challenges will be the emerging introduction of machine-based intelligence”

The inability of the QAs to intelligently select test cases might stem from not having a large volume of test cases in the first place.

Machine-based Intelligence like Spider AI can solve the problem for QAs. Spidering can augment the test coverage by generating optimal test cases to DevOps and QA.

And, shopping like experience would be nice if the QAs are also given recommendations from the pool of test suites like “Test cases you might like”, “More Test cases like this”. And, that’s exactly what Zuci is trying to build with its patented intellectual property.

A good amount of data in forms of past defect history, trends in defects, etc. has to be fed to the machine learning engine, thus helping it in intelligent Test Case selection.

Combined Intelligence can provide a personalized feed of predictive and prescriptive insights into software performance and improve the quality in production environments.

To learn more about the AI trends in software testing, the impact of AI and allied technologies on software testing and how these technologies can be used for the betterment of software testing, join us for the live webinar on “How AI is changing Defect Detection”? on Sept 19, 2019 at 11 AM CST.

Looking for a DevOps testing partner? Talk to us and leave all your DevOps testing tasks to Zuci’s QA experts.

Save your seat here

Read also, Defect Detection using Artificial Intelligence in Software Testing– Learning from other industries

Leave A Comment

Related Posts