The Hexawise Software Testing carnival focuses on sharing interesting and useful blog posts related to software testing.
Getting Started with AI for Testing by Tariq King - "The field of AI is broad and so I recommend that you gain an understanding of that breadth before you do a deep dive into specific areas. Artificial Intelligence: A Modern Approach by Stuart Russell and Peter Norvig is a great reference book for all things AI."
Testing Software with Artificial Intelligence by Ben Linders - "I have always said that tests remove the fear from the development process—removing the fear of adding features to our code, and, even more importantly, removing the fear of refactoring our code. But why should mitigating the fear of code change only be applied to our business logic? Why can’t our visual code (in CSS, HTML, and JS files) also be tested, enabling us to remove the fear of changing it? Advances in AI finally enable us to do this."
Machine Learning for Testers by Angie Jones - "machine learning is a realized form of AI that is currently being utilized by top tech companies in products that we use every day. For this reason, it’s important that we understand how this works and what vulnerabilities we should we aware of as testers."
5 ways AI will change software testing by Paul Merrill - "How will AI testing AI affect us as testers? As Milman and Carmi point out, 'Test engineers would need a different set of skills in order to build and maintain AI-based test suites that test AI-based products. The job requirements would include more focus on data science skills, and test engineers would be required to understand some deep learning principles.'"
AI Test Automation: The AI Test Bots Are Coming by Greg Sypolt - "These days AI is everywhere—from Siri, Alexa and Google Search to Google Assistant, Slackbot, and more. Each of these AI applications has specific roles and goals. In order for AI bots to work, you need to define the specific goal of your AI—whether it’s creating test cases automatically, generating test code, performing codeless tests, or something else."
Playing Atari with Deep Reinforcement Learning - not specifically related to software testing but this research paper provides some insight into what is possible with machine learning. "The model is a convolutional neural network, trained with a variant of Q-learning,whose input is raw pixels and whose output is a value function estimating futurerewards. We apply our method to seven Atari 2600 games from the Arcade Learn-ing Environment, with no adjustment of the architecture or learning algorithm."