Imagine an AI writing tests for software, but how do you know those tests are any good? That's the challenge researchers tackled in a new paper introducing VALTEST, a framework designed to automatically validate test cases generated by Large Language Models (LLMs). Currently, verifying LLM-generated tests requires running them against correct code, a major hurdle when dealing with buggy or not-yet-written software. VALTEST sidesteps this issue by leveraging the subtle signals hidden within the AI's own output. The key insight is that LLMs reveal their confidence through token probabilities—how likely they are to choose a specific word or symbol. VALTEST analyzes these probabilities, extracting statistical features to train a separate machine learning model. This validation model then predicts the likelihood of a generated test being correct, essentially allowing the AI to double-check its own work. Experiments across datasets like HumanEval, MBPP, and LeetCode, using LLMs like GPT-4, GPT-3.5-turbo, and Llama 3.1, revealed a significant boost in test validity. VALTEST increased the rate of correct tests by up to 24%, offering a crucial step toward more reliable AI-driven software testing. The most significant indicator? The LLM's confidence in the expected output of a test, suggesting that wobbly predictions often lead to faulty validations. This research opens doors to a future where AI not only writes our tests but also judges their quality, promising more robust and automated software development. However, the approach isn’t foolproof. Ambiguous code descriptions can trip up VALTEST, and there's a delicate balance between identifying faulty tests and accidentally discarding good ones. Further research into dynamic correction methods and integration with test generation frameworks is needed to solidify AI’s role as both test writer and judge.