When Agents Learn to Test Themselves: TDFlow and the Future of Software Engineering
From Coding to Testing: The Shift in Focus TDFlow, developed by researchers at Carnegie Mellon, UC San Diego, and Johns Hopkins, presents a provocative twist on how we think about AI-driven software engineering. Instead of treating the large language model (LLM) as a creative coder, TDFlow frames the entire process as a test-resolution problem—where the agent’s goal is not to write elegant code, but simply to make the tests pass. ...