Context-Aware Parse Trees

• URL: http://arxiv.org/abs/2003.11118v1
• Date: Tue, 24 Mar 2020 21:19:14 GMT
• Title: Context-Aware Parse Trees
• Authors: Fangke Ye, Shengtian Zhou, Anand Venkat, Ryan Marcus, Paul Petersen, Jesmin Jahan Tithi, Tim Mattson, Tim Kraska, Pradeep Dubey, Vivek Sarkar, Justin Gottschlich
• Abstract summary: We present a new tree structure, heavily influenced by Aroma's SPT, called a emphcontext-aware parse tree (CAPT) CAPT enhances SPT by providing a richer level of semantic representation. Our research quantitatively demonstrates the value of our proposed semantically-salient features, enabling a specific CAPT configuration to be 39% more accurate than SPT across the 48,610 programs we analyzed.
• Score: 18.77504064534521
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The simplified parse tree (SPT) presented in Aroma, a state-of-the-art code recommendation system, is a tree-structured representation used to infer code semantics by capturing program \emph{structure} rather than program \emph{syntax}. This is a departure from the classical abstract syntax tree, which is principally driven by programming language syntax. While we believe a semantics-driven representation is desirable, the specifics of an SPT's construction can impact its performance. We analyze these nuances and present a new tree structure, heavily influenced by Aroma's SPT, called a \emph{context-aware parse tree} (CAPT). CAPT enhances SPT by providing a richer level of semantic representation. Specifically, CAPT provides additional binding support for language-specific techniques for adding semantically-salient features, and language-agnostic techniques for removing syntactically-present but semantically-irrelevant features. Our research quantitatively demonstrates the value of our proposed semantically-salient features, enabling a specific CAPT configuration to be 39\% more accurate than SPT across the 48,610 programs we analyzed.

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