# Neural Software Analysis

arXiv: https://arxiv.org/abs/2011.07986 · HTML: https://mlsanity.com/paper/2011.07986/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Nov 2020.

## Built on by (most cited first, 11 of 11)

- [An Empirical Evaluation of Using Large Language Models for Automated Unit Test Generation](https://mlsanity.com/paper/2302.06527/) (2023)
- [NatGen: Generative pre-training by "Naturalizing" source code](https://mlsanity.com/paper/2206.07585/) (2022)
- [Can LLMs Replace Manual Annotation of Software Engineering Artifacts?](https://mlsanity.com/paper/2408.05534/) (2024)
- [Code Generation Tools (Almost) for Free? A Study of Few-Shot, Pre-Trained Language Models on Code](https://mlsanity.com/paper/2206.01335/) (2022)
- [LExecutor: Learning-Guided Execution](https://mlsanity.com/paper/2302.02343/) (2023)
- [Multi-Granularity Detector for Vulnerability Fixes](https://mlsanity.com/paper/2305.13884/) (2023)
- [NeuDep: Neural Binary Memory Dependence Analysis](https://mlsanity.com/paper/2210.02853/) (2022)
- [Treefix: Enabling Execution with a Tree of Prefixes](https://mlsanity.com/paper/2501.12339/) (2025)
- [On Accelerating Deep Neural Network Mutation Analysis by Neuron and Mutant Clustering](https://mlsanity.com/paper/2501.12598/) (2025)
- [Inferring Properties of Graph Neural Networks](https://mlsanity.com/paper/2401.03790/) (2024)
- [Multi-modal Learning for WebAssembly Reverse Engineering](https://mlsanity.com/paper/2404.03171/) (2024)

## Abstract (arXiv)

Many software development problems can be addressed by program analysis tools, which traditionally are based on precise, logical reasoning and heuristics to ensure that the tools are practical. Recent work has shown tremendous success through an alternative way of creating developer tools, which we call neural software analysis. The key idea is to train a neural machine learning model on numerous code examples, which, once trained, makes predictions about previously unseen code. In contrast to traditional program analysis, neural software analysis naturally handles fuzzy information, such as coding conventions and natural language embedded in code, without relying on manually encoded heuristics. This article gives an overview of neural software analysis, discusses when to (not) use it, and presents three example analyses. The analyses address challenging software development problems: bug detection, type prediction, and code completion. The resulting tools complement and outperform traditional program analyses, and are used in industrial practice.