  The rapid evolution of Web UI incurs time and effort in maintaining UI tests.
Existing techniques in Web UI test repair focus on finding the target elements
on the new web page that match the old ones so that the corresponding broken
statements can be repaired. We present the first study that investigates the
feasibility of using prior Web UI repair techniques for initial local matching
and then using ChatGPT to perform global matching. Our key insight is that
given a list of elements matched by prior techniques, ChatGPT can leverage the
language understanding to perform global view matching and use its code
generation model for fixing the broken statements. To mitigate hallucination in
ChatGPT, we design an explanation validator that checks whether the provided
explanation for the matching results is consistent, and provides hints to
ChatGPT via a self-correction prompt to further improve its results. Our
evaluation on a widely used dataset shows that the ChatGPT-enhanced techniques
improve the effectiveness of existing Web test repair techniques. Our study
also shares several important insights in improving future Web UI test repair
techniques.
