Artificial Intelligence Driven software testing (Turning Testers to ML Experts)
In the near future, many “Software Testers” will transition to ML/AI engineering roles that will focus on software quality instead of just driving cars or finding cats and dogs in photos. ML/AI will fundamentally change how all software testing is performed, but it is a good thing. As a software tester, if you play your cards right, you can have a lot more fun, have a cool title, and finally make the big bucks. Testing ML and AI systems that do not always return the same answers require new approaches to testing. This is especially challenging when testing systems whose responses adapt to what they have learned from previous transactions. To test systems that harness the power of machine learning, QA professionals have to equip themselves with the basics fundamentals of machine learning.
Since AI trains itself via multiple dynamic and static data sources, there can be several issues related to the quality of input data. The data could be incorrect or incomplete. Poor data quality or formatting issues can also pose a challenge. In the case of dynamic data, its variety and velocity could induce errors, and testing such applications is tricky. It is essential to understand the nature of the algorithms to test them properly. AI uses cognitive learning algorithms to enable machine learning and analytics. Success sometimes depends on how data are split for training and testing, and this understanding is the key to proper testing of the AI-driven applications.
This talk will provide some fundamental insights on how to apply machine learning to day-to-day testing activities. Basic machine learning concepts of machine learning will be illustrated with examples of software testing. The session will provide insights into various AI / ML techniques and how they can be used by the software testers to test the applications.