# Multimodal Deep Learning for Flaw Detection in Software Programs

arXiv: https://arxiv.org/abs/2009.04549 · HTML: https://mlsanity.com/paper/2009.04549/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Sep 2020.

## Built on by (most cited first, 2 of 2)

- [Bi-Directional Transformers vs. word2vec: Discovering Vulnerabilities in Lifted Compiled Code](https://mlsanity.com/paper/2405.20611/) (2024)
- [Impact of Data Snooping on Deep Learning Models for Locating Vulnerabilities in Lifted Code](https://mlsanity.com/paper/2412.02048/) (2024)

## Abstract (arXiv)

We explore the use of multiple deep learning models for detecting flaws in software programs. Current, standard approaches for flaw detection rely on a single representation of a software program (e.g., source code or a program binary). We illustrate that, by using techniques from multimodal deep learning, we can simultaneously leverage multiple representations of software programs to improve flaw detection over single representation analyses. Specifically, we adapt three deep learning models from the multimodal learning literature for use in flaw detection and demonstrate how these models outperform traditional deep learning models. We present results on detecting software flaws using the Juliet Test Suite and Linux Kernel.