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In recent times, it has been shown that one can use code as data to aidvarious applications such as automatic commit message generation, automaticgeneration of pull request descriptions and automatic program repair. Take forinstance the problem of commit message generation. Treating source code as asequence of tokens, state of the art techniques generate commit messages usingneural machine translation models. However, they tend to ignore the syntacticstructure of programming languages.  Previous work, i.e., code2seq has used structural information from AbstractSyntax Tree (AST) to represent source code and they use it to automaticallygenerate method names. In this paper, we elaborate upon this state of the artapproach and modify it to represent source code edits. We determine the effectof using such syntactic structure for the problem of classifying code edits.Inspired by the code2seq approach, we evaluate how using structural informationfrom AST, i.e., paths between AST leaf nodes can help with the task of codeedit classification on two datasets of fine-grained syntactic edits.  Our experiments shows that attempts of adding syntactic structure does notresult in any improvements over less sophisticated methods. The results suggestthat techniques such as code2seq, while promising, have a long way to go beforethey can be generically applied to learning code edit representations. We hopethat these results will benefit other researchers and inspire them to workfurther on this problem.

Assessing the Effectiveness of Syntactic Structure to Learn Code Edit Representations