School of Computer Science and Statistics
Testing software means systematically probing it for the edge cases that break it — but RL policies don’t come with a specification to test against, and their failures often only surface deep into a long trajectory, making them hard to catch before deployment. Counterfactual explanation techniques developed in this group already show that tracing “what … Read more
Large language models can be used as autonomous agents that interact with applications through a browser, potentially allowing tests to be expressed as high-level goals rather than detailed scripts. This project’s vision is to investigate whether such agents can effectively explore and test web applications, and which factors affect their performance. The idea could be … Read more
Automated tests can sometimes become flaky, passing and failing across executions even when the software has not changed, reducing confidence in Software Testing and Continuous Integration. Recent studies have shown that web test flakiness can arise from factors such as asynchronous DOM interactions, timing assumptions, shared state, and test-order dependencies. The aim of this project … Read more