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This paper discusses the limitations of evaluating Masked Language Models(MLMs) in code completion tasks. We highlight that relying on accuracy-basedmeasurements may lead to an overestimation of models' capabilities byneglecting the syntax rules of programming languages. To address these issues,we introduce a technique called SyntaxEval in which Syntactic Capabilities areused to enhance the evaluation of MLMs. SyntaxEval automates the process ofmasking elements in the model input based on their Abstract Syntax Trees(ASTs). We conducted a case study on two popular MLMs using data from GitHubrepositories. Our results showed negative causal effects between the node typesand MLMs' accuracy. We conclude that MLMs under study fail to predict somesyntactic capabilities.

Which Syntactic Capabilities Are Statistically Learned by Masked Language Models for Code?