Conference paper Open Access

A Structural Model for Contextual Code Changes

Brody, Shaked; Alon, Uri; Yahav, Eran

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{
"publisher": "Zenodo",
"DOI": "10.5281/zenodo.4036303",
"title": "A Structural Model for Contextual Code Changes",
"issued": {
"date-parts": [
[
2020,
9,
18
]
]
},
"abstract": "<p>We address the problem of predicting edit completions based on a learned model that was trained on past edits.<br>\nGiven a code snippet that is partially edited, our goal is to predict a completion of the edit for the rest of the<br>\nsnippet. We refer to this task as the EditCompletion&nbsp;task and present a novel approach for tackling it. The<br>\nmain idea is to directly represent structural edits. This allows us to model the likelihood of the edit itself, rather<br>\nthan learning the likelihood of the edited code. We represent an edit operation as a path in the program&rsquo;s Abstract<br>\nSyntax Tree (AST), originating from the source of the edit to the target of the edit. Using this representation, we<br>\npresent a powerful and lightweight neural model for the EditCompletion&nbsp;task.</p>\n\n<p><br>\nWe conduct a thorough evaluation, comparing our approach to a variety of representation and modeling<br>\napproaches that are driven by multiple strong models such as LSTMs, Transformers, and neural CRFs. Our<br>\nexperiments show that our model achieves 28% relative gain over state-of-the-art sequential models and 2&times;<br>\nhigher accuracy than syntactic models that learn to generate the edited code instead of modeling the edits<br>\ndirectly. We make our code, dataset, and trained models publicly available.</p>",
"author": [
{
"family": "Brody, Shaked"
},
{
"family": "Alon, Uri"
},
{
"family": "Yahav, Eran"
}
],
"id": "4036303",
"type": "paper-conference",
"event": "Object-Oriented Programming, Systems, Languages and Applications (OOPSLA)"
}
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