Rutvik Mrug

Topic: Modern QA through modernization of data

Abstract: Technology has taken giant leap in last decade, we are also proud on how QA has equally come stronger. Unfortunately, One problem is consistent– i.e,, data for testing (and even for development in case of machine learning). This is echoed in last few years of the World Quality Report where test data and environment are the major impediment for effective and efficient QA.

In fact, focusing on data is need of hour as–
- AI is all about data, from training to testing
- Continuous testing will be ineffective without data automation and right data
- IOT and Microservice are based on different behaviour and volume of data

We still have hardly evolved in the way we tackled data. To modernize data, we have to adopt modern techniques to treat data like –
- AI to generate to help building AI system (training / testing data)
- Containerization of “bookmarked” data for faster data fulfilment
- API based Orchestration engine to produce E2E quality data
- Data visualization for available data
In fact, Data is the big difference between what we envision QA strategy to success of such implementation. Like application is getting modernized by adopting levers of cloud, AI, microservice, the way data treated has to be modernized to achieve common goal of modern QA.