上下文感知解析树
Context-Aware Parse Trees
摘要：
Aroma是一种最先进的代码推荐系统，它提出的简化解析树（SPT）是一种树结构表示，用于通过捕获程序\emph{structure}而不是程序\emph{syntax}来推断代码语义。这与主要由程序设计语言语法驱动的经典抽象语法树不同。虽然我们相信语义驱动的表示是可取的，一个SPT的建设的细节可以影响其性能。我们分析了这些细微差别，并提出了一个新的树结构，严重影响芳香的SPT，称为\emph{context-aware parse tree}（CAPT）。CAPT通过提供更丰富层次的语义表示来增强SPT。具体地说，CAPT为特定于语言的技术提供了额外的绑定支持，用于添加语义突出的功能，并为语言无关的技术提供了额外的绑定支持，用于删除语法上存在但语义上不相关的功能。我们的研究定量地证明了我们提出的语义显著特征的价值，使特定的CAPT配置在我们分析的48，610个程序中比SPT准确39%。
英文摘要：
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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