Behavioral testing in NLP permits fine-grained analysis of methods by inspecting their linguistic capabilities by way of the evaluation of input-output habits. Sadly, present work on behavioral testing in Machine Translation (MT) is at present restricted to largely handcrafted exams protecting a restricted vary of capabilities and languages. To deal with this limitation, we suggest utilizing Massive Language Fashions (LLMs) to generate a various set of supply sentences tailor-made to check the habits of MT fashions in a variety of conditions. We will then confirm whether or not the MT mannequin displays the anticipated habits by way of matching candidate units which might be additionally generated utilizing LLMs. Our method goals to make behavioral testing of MT methods sensible whereas requiring solely minimal human effort. In our experiments, we apply our proposed analysis framework to evaluate a number of out there MT methods, revealing that whereas on the whole cross charges observe the tendencies observable from conventional accuracy-based metrics, our methodology was capable of uncover a number of vital variations and potential bugs that go unnoticed when relying solely on accuracy.