# μBERT: Mutation Testing using Pre-Trained Language Models

arXiv: https://arxiv.org/abs/2203.03289 · HTML: https://mlsanity.com/paper/2203.03289/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Mar 2022.

## Built on by (most cited first, 3 of 3)

- [Code Generation Tools (Almost) for Free? A Study of Few-Shot, Pre-Trained Language Models on Code](https://mlsanity.com/paper/2206.01335/) (2022)
- [Turbulence: Systematically and Automatically Testing Instruction-Tuned Large Language Models for Code](https://mlsanity.com/paper/2312.14856/) (2023)
- [NeuDep: Neural Binary Memory Dependence Analysis](https://mlsanity.com/paper/2210.02853/) (2022)

## Abstract (arXiv)

We introduce μBERT, a mutation testing tool that uses a pre-trained language model (CodeBERT) to generate mutants. This is done by masking a token from the expression given as input and using CodeBERT to predict it. Thus, the mutants are generated by replacing the masked tokens with the predicted ones. We evaluate μBERT on 40 real faults from Defects4J and show that it can detect 27 out of the 40 faults, while the baseline (PiTest) detects 26 of them. We also show that μBERT can be 2 times more cost-effective than PiTest, when the same number of mutants are analysed. Additionally, we evaluate the impact of μBERT's mutants when used by program assertion inference techniques, and show that they can help in producing better specifications. Finally, we discuss about the quality and naturalness of some interesting mutants produced by μBERT during our experimental evaluation.