Predicting program properties such as names or expression types has a wide
range of applications. It can ease the task of programming and increase
programmer productivity. A major challenge when learning from programs is
$\textit{how to represent programs in a way that facilitates effective
learning}$.
We present a $\textit{general path-based representation}$ for learning from
programs. Our representation is purely syntactic and extracted automatically.
The main idea is to represent a program using paths in its abstract syntax tree
(AST). This allows a learning model to leverage the structured nature of code
rather than treating it as a flat sequence of tokens.
We show that this representation is general and can: (i) cover different
prediction tasks, (ii) drive different learning algorithms (for both generative
and discriminative models), and (iii) work across different programming
languages.
We evaluate our approach on the tasks of predicting variable names, method
names, and full types. We use our representation to drive both CRF-based and
word2vec-based learning, for programs of four languages: JavaScript, Java,
Python and C\#. Our evaluation shows that our approach obtains better results
than task-specific handcrafted representations across different tasks and
programming languages.

提出一种基于路径的编程语言自动学习表示方法，其将程序表示为其抽象语法树中的路径，可用于预测变量名称、方法名称和完全类型，并可跨不同编程语言驱动不同的学习算法。

基于路径的通用程序属性预测表示

A General Path-Based Representation for Predicting Program Properties

Constraint Handling Rules (CHR) is a declarative committed-choice programming
language with a strong relationship to linear logic. Its generalization CHR
with Disjunction (CHRv) is a multi-paradigm declarative programming language
that allows the embedding of horn programs. We analyse the assets and the
limitations of the classical declarative semantics of CHR before we motivate
and develop a linear-logic declarative semantics for CHR and CHRv. We show how
to apply the linear-logic semantics to decide program properties and to prove
operational equivalence of CHRv programs across the boundaries of language
paradigms.

本文关注 Constraint Handling Rules（CHR）及其拓展 CHRv 的声明式语言，分析其经典的声明式语义的优缺点，提出并开发基于线性逻辑的声明式语义，探讨如何将其应用于决定程序属性和证明跨语言范式的 CHRv 程序的运行等价性。