Testing

As AI advances, we find ourselves heading towards a new era. By living and working with AI technologies, we will operate at a level far beyond our previous potential. This will create a paradigm shift for us and positively impacting everything. With such exponential opportunity, we must fundamentally rethink the Future of QA and how people lead, grow and change in this new context.

Running an application in a target platform includes several stages starting from coding to staging including static code analysis, building, testing and monitoring workflows. In every single step, we can use several different tools. In the ideal scenario, when we push our code, it goes through a pipeline where it is styled and formatted, built, packed, installed, tested, integrated and verified. Sounds crazy, we just push the code and all those listed jobs are executed automatically in push on green flows.

This dream scenario can easily turn into a nightmare. If the pipeline is broken frequently, or running slowly, or is not covering the whole scope; we may start worrying about it. If the pipeline is not failing at all, that is not good news either. It may be a sign to check the quality of tests.

Learnings from this session include key take-aways such as:

A checklist to choose automation tools and make the best use of them
Tips to get rid of flakiness in the deployment pipelines
A guideline to build a quality mindset and tips for a strong quality gate
and a set of sample quality metrics to track the product and process maturity