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Since regular expressions (abbrev. regexes) are difficult to understand andcompose, automatically generating regexes has been an important researchproblem. This paper introduces TransRegex, for automatically constructingregexes from both natural language descriptions and examples. To the best ofour knowledge, TransRegex is the first to treat the NLP-and-example-based regexsynthesis problem as the problem of NLP-based synthesis with regex repair. Forthis purpose, we present novel algorithms for both NLP-based synthesis andregex repair. We evaluate TransRegex with ten relevant state-of-the-art toolson three publicly available datasets. The evaluation results demonstrate thatthe accuracy of our TransRegex is 17.4%, 35.8% and 38.9% higher than that ofNLP-based approaches on the three datasets, respectively. Furthermore,TransRegex can achieve higher accuracy than the state-of-the-art multi-modaltechniques with 10% to 30% higher accuracy on all three datasets. Theevaluation results also indicate TransRegex utilizing natural language andexamples in a more effective way.

TransRegex: Multi-modal Regular Expression Synthesis by Generate-and-Repair