Over the last year, DevOps--the seamless integration of software development teams and IT operations teams--has become an increasingly strategic, mission critical, business function. On one hand, this means that DevOps now has a seat at the top table. Decisions they take are recognised as make or break for businesses who strive to deliver quality products and services at high velocity. On the other hand, it also means that the pressure is on. Enterprises will look to find ways to integrate the voice of the business into the DevOps process, and they will also begin to measure DevOps success on how it affects the bottom line.
So, with so much riding on the success of DevOps, and in the lead up to a new year, with new challenges and opportunities looming large, we wanted to take a look at the game changing innovations that development teams can harness.
Al and machine learning will speed up DevOps quality analysis:
Some experts believe that Machine learning (ML) is poised to change the nature of software development in fundamental ways. Development teams need need to analyse more data and are given less time to do it, while their margin of error also decreases constantly. And so, tools such as machine learning and predictive analytics offer a way to address these challenges by being able to process data much more quickly and thoroughly.
Crucially, ML and Al solutions will automate the slicing and dicing of data to quickly provide root-cause analysis for issues that are detected during the DevOps pipeline testing activities. At Perfecto, we believe that up to 80 percent of issues have a pattern, so being able to categorise them is important. Are twenty per cent of my errors related to poor coding? Is a similar percentage due to security issues? Being able spot patterns and usefully classify them is an important part of eliminating errors and smoothing the testing pipeline.
Away from the lab, businesses need to find a way to be a step ahead of their competitors and be able to predict their consumers' needs. Predictive analytics plays a key role here as it allows businesses to analyse customer data to better understand (and predict) what new products and features they might want next.
Of course, embracing ML, making the most of data relies on a fundamental mindset shift. In all business areas there is a fear that the "bots are coming", but we believe that occupations are more likely to be transformed by digital technology than destroyed by...