# DeepRNG: Towards Deep Reinforcement Learning-Assisted Generative Testing of Software

arXiv: https://arxiv.org/abs/2201.12602 · HTML: https://mlsanity.com/paper/2201.12602/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 18 references that are in the map.
- First posted on arXiv 29 Jan 2022.

## Builds on (most cited first, 9 of 9)

- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures](https://mlsanity.com/paper/1802.01561/) (2018)
- [First return, then explore](https://mlsanity.com/paper/2004.12919/) (2020)
- [A Survey of Machine Learning for Big Code and Naturalness](https://mlsanity.com/paper/1709.06182/) (2017)
- [Go-Explore: a New Approach for Hard-Exploration Problems](https://mlsanity.com/paper/1901.10995/) (2019)
- [NEUZZ: Efficient Fuzzing with Neural Program Smoothing](https://mlsanity.com/paper/1807.05620/) (2018)
- [A systematic review of fuzzing based on machine learning techniques](https://mlsanity.com/paper/1908.01262/) (2019)
- [Learning Transferable Graph Exploration](https://mlsanity.com/paper/1910.12980/) (2019)
- [A Review of Machine Learning Applications in Fuzzing](https://mlsanity.com/paper/1906.11133/) (2019)

## Abstract (arXiv)

Although machine learning (ML) has been successful in automating various software engineering needs, software testing still remains a highly challenging topic. In this paper, we aim to improve the generative testing of software by directly augmenting the random number generator (RNG) with a deep reinforcement learning (RL) agent using an efficient, automatically extractable state representation of the software under test. Using the Cosmos SDK as the testbed, we show that the proposed DeepRNG framework provides a statistically significant improvement to the testing of the highly complex software library with over 350,000 lines of code. The source code of the DeepRNG framework is publicly available online.