# PSIMiner: A Tool for Mining Rich Abstract Syntax Trees from Code

arXiv: https://arxiv.org/abs/2103.12778 · HTML: https://mlsanity.com/paper/2103.12778/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 24 references that are in the map.
- First posted on arXiv 23 Mar 2021.

## Builds on (most cited first, 10 of 10)

- [Learning to Represent Programs with Graphs](https://mlsanity.com/paper/1711.00740/) (2017)
- [code2seq: Generating Sequences from Structured Representations of Code](https://mlsanity.com/paper/1808.01400/) (2018)
- [Deep Learning for Source Code Modeling and Generation: Models, Applications and Challenges](https://mlsanity.com/paper/2002.05442/) (2020)
- [Structured Neural Summarization](https://mlsanity.com/paper/1811.01824/) (2018)
- [A Survey on Deep Learning for Software Engineering](https://mlsanity.com/paper/2011.14597/) (2020)
- [A General Path-Based Representation for Predicting Program Properties](https://mlsanity.com/paper/1803.09544/) (2018)
- [Typilus: Neural Type Hints](https://mlsanity.com/paper/2004.10657/) (2020)
- [Automatic Source Code Summarization with Extended Tree-LSTM](https://mlsanity.com/paper/1906.08094/) (2019)
- [Deep Learning in Software Engineering](https://mlsanity.com/paper/1805.04825/) (2018)
- [Software Engineering Meets Deep Learning: A Mapping Study](https://mlsanity.com/paper/1909.11436/) (2019)

## Built on by (most cited first, 4 of 4)

- [Large Language Models (LLMs) for Source Code Analysis: applications, models and datasets](https://mlsanity.com/paper/2503.17502/) (2025)
- [A Survey of Source Code Representations for Machine Learning-Based Cybersecurity Tasks](https://mlsanity.com/paper/2403.10646/) (2024)
- [Assessing Project-Level Fine-Tuning of ML4SE Models](https://mlsanity.com/paper/2206.03333/) (2022)
- [Evaluating the Impact of Source Code Parsers on ML4SE Models](https://mlsanity.com/paper/2206.08713/) (2022)

## Abstract (arXiv)

The application of machine learning algorithms to source code has grown in the past years. Since these algorithms are quite sensitive to input data, it is not surprising that researchers experiment with input representations. Nowadays, a popular starting point to represent code is abstract syntax trees (ASTs). Abstract syntax trees have been used for a long time in various software engineering domains, and in particular in IDEs. The API of modern IDEs allows to manipulate and traverse ASTs, resolve references between code elements, etc. Such algorithms can enrich ASTs with new data and therefore may be useful in ML-based code analysis. In this work, we present PSIMiner - a tool for processing PSI trees from the IntelliJ Platform. PSI trees contain code syntax trees as well as functions to work with them, and therefore can be used to enrich code representation using static analysis algorithms of modern IDEs. To showcase this idea, we use our tool to infer types of identifiers in Java ASTs and extend the code2seq model for the method name prediction problem.