It’s (been) Time to Use Machine Learning to Fix End to End Testing

Too Long; Didn't Read

I’m a serial entrepreneur who’s spent time working at Google, VMware, Stackdriver and now my second startup I co-founded, <a href="https://www.mabl.com/" target="_blank">mabl</a>. We started mabl earlier this year to help developers with the painful challenge of testing their applications, especially as development cycles are getting shorter with the adoption of CI/CD and continued automation. As we started the company, I did a lot of research, primarily by reaching out to developers to understand their take on software testing, especially those that have embraced DevOps and we also launched a <a href="https://www.surveymonkey.com/r/longreen" target="_blank">survey</a> to get quantitative feedback. In addition, I also found lots of blogs written about the challenges of testing applications in a modern developer workflow. One of the blog posts that I was drawn to was from <a href="https://www.linkedin.com/in/mike-wacker-a1224395/" target="_blank">Mike Wacker</a>, a software engineer at Google who was also a developer at Microsoft previously. Mike wrote a <a href="https://testing.googleblog.com/2015/04/just-say-no-to-more-end-to-end-tests.html?m=1" target="_blank">blog</a> post in 2015 about why End to End Tests just don’t matter anymore and developers should focus on Unit Tests primarily, and to a lesser extent, Integration Tests, as the basis for ensuring quality in their code. If you haven’t read it and are involved in testing, you should. His premise was (I’m summarizing and probably not doing enough justice to Mike):