“Navigating AI-Based Quality Assurance Automation with Infosys”: A blog by NelsonHall
Dominique Raviart, IT Services Practice Director, NelsonHall has published a blog on how Infosys has continued investment in AI to automate testing and validate chatbots and AI models. In the blog, Dominique talks about the advantages of incorporating AI in testing and the challenges that come with it. He also highlights Infosys’ approach towards validation of AI, including AI-models.
Key highlights from the blog:
- Infosys has developed a repository of ~500 testing-specific RPA bots to automate manual tasks; an example is a bot for setting up alerts on test execution monitoring dashboards, and another is loading test cases to test management tools such as JIRA
- Infosys will identify if an application release has a screen change such as a field or button changing place and will update the script accordingly.
- Infosys points to the increasing integration of chatbot functionality within AR/VR. This integration is bringing another layer of QA complexity and performance discussions. Infosys is taking a systematic approach to chatbot testing and has built several accelerators around voice utterances.
- Infosys is approaching the AI-model QA from several angles. For training and testing purposes, data plays an essential role in the accuracy of data science models. Infosys is creating synthetic data for training models, taking patterns from production data. With this approach, it is solving the challenge of the lack of sufficient data for training the AI model.
- Another approach that Infosys is taking is a statistical method. It provides a series of statistical measures to data scientists, who can then decide on the accuracy of the data model.
Read the complete blog here.