# A General Path-Based Representation for Predicting Program Properties

arXiv: https://arxiv.org/abs/1803.09544 · HTML: https://mlsanity.com/paper/1803.09544/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Mar 2018.

## Built on by (most cited first, 29 of 29)

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- [PSIMiner: A Tool for Mining Rich Abstract Syntax Trees from Code](https://mlsanity.com/paper/2103.12778/) (2021)
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- [AdaptivePaste: Code Adaptation through Learning Semantics-aware Variable Usage Representations](https://mlsanity.com/paper/2205.11023/) (2022)
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- [CLAWSAT: Towards Both Robust and Accurate Code Models](https://mlsanity.com/paper/2211.11711/) (2022)
- [MIREncoder: Multi-modal IR-based Pretrained Embeddings for Performance Optimizations](https://mlsanity.com/paper/2407.02238/) (2024)
- [Dissecting model behavior through agent trajectories](https://mlsanity.com/paper/2606.17454/) (2026)
- [Evaluating the Impact of Source Code Parsers on ML4SE Models](https://mlsanity.com/paper/2206.08713/) (2022)
- [An Exploratory Literature Study on Sharing and Energy Use of Language Models for Source Code](https://mlsanity.com/paper/2307.02443/) (2023)
- [Code Representation Learning with Pr"ufer Sequences](https://mlsanity.com/paper/2111.07263/) (2021)
- [Modelling Concurrency Bugs Using Machine Learning](https://mlsanity.com/paper/2305.05531/) (2023)
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## Abstract (arXiv)

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 textithow to represent programs in a way that facilitates effective learning. We present a textitgeneral 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.