# Testing Neural Program Analyzers

arXiv: https://arxiv.org/abs/1908.10711 · HTML: https://mlsanity.com/paper/1908.10711/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Aug 2019.

## Built on by (most cited first, 1 of 1)

- [CatCode: A Comprehensive Evaluation Framework for LLMs On the Mixture of Code and Text](https://mlsanity.com/paper/2403.01784/) (2024)

## Abstract (arXiv)

Deep neural networks have been increasingly used in software engineering and program analysis tasks. They usually take a program and make some predictions about it, e.g., bug prediction. We call these models neural program analyzers. The reliability of neural programs can impact the reliability of the encompassing analyses. In this paper, we describe our ongoing efforts to develop effective techniques for testing neural programs. We discuss the challenges involved in developing such tools and our future plans. In our preliminary experiment on a neural model recently proposed in the literature, we found that the model is very brittle, and simple perturbations in the input can cause the model to make mistakes in its prediction.