# Syntax-Guided Program Reduction for Understanding Neural Code Intelligence Models

arXiv: https://arxiv.org/abs/2205.14374 · HTML: https://mlsanity.com/paper/2205.14374/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 29 references that are in the map.
- First posted on arXiv 28 May 2022.

## Builds on (most cited first, 18 of 18)

- [code2vec: Learning Distributed Representations of Code](https://mlsanity.com/paper/1803.09473/) (2018)
- [A Survey of Machine Learning for Big Code and Naturalness](https://mlsanity.com/paper/1709.06182/) (2017)
- [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)
- [The Adverse Effects of Code Duplication in Machine Learning Models of Code](https://mlsanity.com/paper/1812.06469/) (2018)
- [A Convolutional Attention Network for Extreme Summarization of Source Code](https://mlsanity.com/paper/1602.03001/) (2016)
- [Adversarial Examples for Models of Code](https://mlsanity.com/paper/1910.07517/) (2019)
- [On the Generalizability of Neural Program Models with respect to Semantic-Preserving Program Transformations](https://mlsanity.com/paper/2008.01566/) (2020)
- [Learning Natural Coding Conventions](https://mlsanity.com/paper/1402.4182/) (2014)
- [A Survey on Machine Learning Techniques for Source Code Analysis](https://mlsanity.com/paper/2110.09610/) (2021)
- [Understanding Neural Code Intelligence Through Program Simplification](https://mlsanity.com/paper/2106.03353/) (2021)
- [Memorization and Generalization in Neural Code Intelligence Models](https://mlsanity.com/paper/2106.08704/) (2021)
- [A Literature Study of Embeddings on Source Code](https://mlsanity.com/paper/1904.03061/) (2019)
- [Probing Model Signal-Awareness via Prediction-Preserving Input Minimization](https://mlsanity.com/paper/2011.14934/) (2020)
- [Embedding Java Classes with code2vec: Improvements from Variable Obfuscation](https://mlsanity.com/paper/2004.02942/) (2020)
- [Evaluation of Generalizability of Neural Program Analyzers under Semantic-Preserving Transformations](https://mlsanity.com/paper/2004.07313/) (2020)
- [Towards Demystifying Dimensions of Source Code Embeddings](https://mlsanity.com/paper/2008.13064/) (2020)
- [Code2Snapshot: Using Code Snapshots for Learning Representations of Source Code](https://mlsanity.com/paper/2111.01097/) (2021)

## Built on by (most cited first, 7 of 7)

- [Reducing Cost of LLM Agents with Trajectory Reduction](https://mlsanity.com/paper/2509.23586/) (2025)
- [The Hidden Cost of Readability: How Code Formatting Silently Consumes Your LLM Budget](https://mlsanity.com/paper/2508.13666/) (2025)
- [A Critical Study of What Code-LLMs (Do Not) Learn](https://mlsanity.com/paper/2406.11930/) (2024)
- [Study of Distractors in Neural Models of Code](https://mlsanity.com/paper/2303.01739/) (2023)
- [Redundancy and Concept Analysis for Code-trained Language Models](https://mlsanity.com/paper/2305.00875/) (2023)
- [Beyond Fidelity: Explaining Vulnerability Localization of Learning-based Detectors](https://mlsanity.com/paper/2401.02686/) (2024)
- [From User Interface to Agent Interface: Efficiency Optimization of UI Representations for LLM Agents](https://mlsanity.com/paper/2512.13438/) (2025)

## Abstract (arXiv)

Neural code intelligence (CI) models are opaque black-boxes and offer little insight on the features they use in making predictions. This opacity may lead to distrust in their prediction and hamper their wider adoption in safety-critical applications. Recently, input program reduction techniques have been proposed to identify key features in the input programs to improve the transparency of CI models. However, this approach is syntax-unaware and does not consider the grammar of the programming language. In this paper, we apply a syntax-guided program reduction technique that considers the grammar of the input programs during reduction. Our experiments on multiple models across different types of input programs show that the syntax-guided program reduction technique is faster and provides smaller sets of key tokens in reduced programs. We also show that the key tokens could be used in generating adversarial examples for up to 65% of the input programs.