Large Language Models (LLMs) are increasingly used to refactor automatically generated unit tests, which often achieve high coverage but suffer from poor readability and maintainability. Evaluating such refactorings remains challenging, as existing metrics either penalize beneficial edits or overlook readability. We introduce CTSES, a composite metric combining CodeBLEU, METEOR, and ROUGE-L to balance semantics, readability, and structure. Experiments on 5,000+ refactorings from Defects4J and SF110 show that CTSES reduces false negatives and provides more interpretable evaluation.