Papers with FuzzAug
FuzzAug: Data Augmentation by Coverage-guided Fuzzing for Neural Test Generation (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Using large language models to generate meaningful tests is expensive and time-consuming . |
| Approach: | They propose a data augmentation technique that incorporates valid testing semantics and diverse coverage-guided inputs into large language models. |
| Outcome: | The proposed technique improves performance over the baselines by incorporating valid testing semantics and providing diverse coverage-guided inputs. |