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Pre-trained language models are effective in a variety of natural languagetasks, but it has been argued their capabilities fall short of fully learningmeaning or understanding language. To understand the extent to which languagemodels can learn some form of meaning, we investigate their ability to capturesemantics of code beyond superficial frequency and co-occurrence. In contrastto previous research on probing models for linguistic features, we studypre-trained models in a setting that allows for objective and straightforwardevaluation of a model's ability to learn semantics. In this paper, we examinewhether such models capture the semantics of code, which is precisely andformally defined. Through experiments involving the manipulation of codefragments, we show that code pre-trained models of code learn a robustrepresentation of the computational semantics of code that goes beyondsuperficial features of form alone

Towards Understanding What Code Language Models Learned