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

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...

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Vydáno v:Proceedings / International Conference on Software Engineering s. 1210 - 1222
Hlavní autoři: Li, Yeting, Li, Shuaimin, Xu, Zhiwu, Cao, Jialun, Chen, Zixuan, Hu, Yun, Chen, Haiming, Cheung, Shing-Chi
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.05.2021
Témata:
Maintenance engineering
Natural languages
programming by example
programming by natural languages
regex repair
regex synthesis
Software engineering
ISBN:1665402962, 9781665402965
ISSN:1558-1225
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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.
AbstractList 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.
Author Hu, Yun
Li, Yeting
Cheung, Shing-Chi
Cao, Jialun
Xu, Zhiwu
Chen, Haiming
Li, Shuaimin
Chen, Zixuan
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  organization: The Hong Kong University of Science and Technology, China
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Snippet Since regular expressions (abbrev. regexes) are difficult to understand and compose, automatically generating regexes has been an important research problem....
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StartPage 1210
SubjectTerms Maintenance engineering
Natural languages
programming by example
programming by natural languages
regex repair
regex synthesis
Software engineering
Title TransRegex: Multi-modal Regular Expression Synthesis by Generate-and-Repair
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