May 21, 2024
The Future of AI-Powered QA Testing
Explore the applications of AI agent workflows to quality assurance
Arvind Subramanian
Current state-of-the-art in QA
Since the 90s, automated software testing has aimed to simplify the repeated workflows conducted by manual testing. This has opened up the position of QA engineering, a role that sits close to the development team to build and maintain automations. Today, this role plays a crucial part in the software development lifecycle.
As software has grown more complex, so too have the frameworks used to test it. With the creation of frameworks like Playwright and Cucumber, the testing industry has made browser automations accessible to technical and non-technical stakeholders. Most companies have found, however, that scaling QA operations still requires scaling a large QA team, despite these frameworks.
This is primarily because the process of creating and maintaining a test is largely manual or engineering-intensive.
AI Agents
AI agents have received a lot of attention in the past few months. They aim to bring human-level functionality to tasks via planning, reasoning, and a working memory. As foundational large-language and large-action models improve, the applications of these agents grow more complex.
Some notable applications of AI agents include:
Workflow automation
Data extraction
Personalized user experiences