The industrial revolution day by day is replacing many manual jobs as we speak for tomorrow’s betterment. Somewhere we can believe that it is impacting our jobs. However artificial intelligence and machine learning, that the world shook hands with will be significantly different from the industrial revolution.
Let’s take a look at some of the nuances of AI and its impact on Software Testing.
At the changing times, the internet was considered luxury. We remember the dial-up 56kbps connection. Those were the days where iPhones/android did not exist neither did google/uber.
The Origin of Quality assurance
Until the dawn of the century, the role of a tester was to come in towards the end of SDLC. By the time tester started testing an application, the underlying business requirements would have changed or may not be relevant at all.
Agile comes rushing through the door
Testers are now an integral part of the team whose presence is early in the software development process. Agile made as 2–3 weeks of development as a norm and testing was no longer the last moment activity. Agile testing replaced classic testing. Agile became an important aspect quickly while XP, scrum, kanban became a standard process in SDLC. Many Agile Project Management tools (open source) like JIRA came into existence. Test Plans and Weekly Test Status reports gave way to storyboards, retrospections.
Continuous testing, CI & CD
Today a vast majority of the organizations focus on constant delivery and constant testing. There has been an explosion of tools around project teams collaboration, automation, application performance monitoring and security testing.
Why AI makes sense for testing?
The main problem with today’s QA lies in the sheer amount of data that testers need to handle in a limited period of time they usually have. Spreadsheets can’t handle it alone. Testing thousands of regression test cases that can take hours and sometimes days. Since clients have become furthermore demanding, traditional testing methods often cannot keep up with them. This often takes traditional testing methods out of the equation and calls for a more advanced approach. That is, the one powered by AI — artificial intelligence.