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Recent years have seen the rise of Deep Learning (DL) techniques applied tosource code. Researchers have exploited DL to automate several development andmaintenance tasks, such as writing commit messages, generating comments anddetecting vulnerabilities among others. One of the long lasting dreams ofapplying DL to source code is the possibility to automate non-trivial codingactivities. While some steps in this direction have been taken (e.g., learninghow to fix bugs), there is still a glaring lack of empirical evidence on thetypes of code changes that can be learned and automatically applied by DL. Ourgoal is to make this first important step by quantitatively and qualitativelyinvestigating the ability of a Neural Machine Translation (NMT) model to learnhow to automatically apply code changes implemented by developers during pullrequests. We train and experiment with the NMT model on a set of 236k pairs ofcode components before and after the implementation of the changes provided inthe pull requests. We show that, when applied in a narrow enough context (i.e.,small/medium-sized pairs of methods before/after the pull request changes), NMTcan automatically replicate the changes implemented by developers during pullrequests in up to 36% of the cases. Moreover, our qualitative analysis showsthat the model is capable of learning and replicating a wide variety ofmeaningful code changes, especially refactorings and bug-fixing activities. Ourresults pave the way for novel research in the area of DL on code, such as theautomatic learning and applications of refactoring.

On Learning Meaningful Code Changes via Neural Machine Translation