TransRegex: Multi-modal Regular Expression Synthesis by Generate-and-Repair
Abstract
Since regular expressions (abbrev. regexes) are difficult to understand and compose, automatically generating regexes has been an important research problem. This paper introduces TransRegex, for automatically constructing regexes from both natural language descriptions and examples. To the best of our knowledge, TransRegex is the first to treat the NLP-and-example-based regex synthesis problem as the problem of NLP-based synthesis with regex repair. For this purpose, we present novel algorithms for both NLP-based synthesis and regex repair. We evaluate TransRegex with ten relevant state-of-the-art tools on three publicly available datasets. The evaluation results demonstrate that the accuracy of our TransRegex is 17.4%, 35.8% and 38.9% higher than that of NLP-based approaches on the three datasets, respectively. Furthermore, TransRegex can achieve higher accuracy than the state-of-the-art multi-modal techniques with 10% to 30% higher accuracy on all three datasets. The evaluation results also indicate TransRegex utilizing natural language and examples in a more effective way.
Cite
@article{arxiv.2012.15489,
title = {TransRegex: Multi-modal Regular Expression Synthesis by Generate-and-Repair},
author = {Yeting Li and Shuaimin Li and Zhiwu Xu and Jialun Cao and Zixuan Chen and Yun Hu and Haiming Chen and Shing-Chi Cheung},
journal= {arXiv preprint arXiv:2012.15489},
year = {2021}
}
Comments
accepted by ICSE 2021