Learning k-Occurrence Regular Expressions with Interleaving

Li, Yeting; Zhang, Xiaolan; Cao, Jialun; Chen, Haiming; Gao, Chong

doi:10.1007/978-3-030-18579-4_5

Cited by 4 publications

(3 citation statements)

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“…Regex synthesis from examples. The problem of automatic regex synthesis from examples has been explored in many domains [4], [16]- [20], [30], [31]. AlphaRegex [19] is a searchbased algorithm for synthesizing simple regexes for introductory automata assignments.…”

Section: A Regex Synthesismentioning

confidence: 99%

“…The fact that RegexGenerator++ utilizes genetic programming means that it is not guaranteed to generate a correct solution-i.e., accepting all the positive examples while rejecting all the negative examples. Lots of existing works focus on XML schemas inference [16]- [18], [31], via resorting to infer regexes from examples. These approaches usually aim to tackle restricted forms of regexes from positive examples only.…”

Section: A Regex Synthesismentioning

confidence: 99%

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

et al. 2020

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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-theart 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 stateof-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.

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Section: A Regex Synthesismentioning

confidence: 99%

Section: A Regex Synthesismentioning

confidence: 99%

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

et al. 2020

Preprint

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show abstract

“…The fact that RegexGenerator++ utilizes genetic programming means that it is not guaranteed to gener ate a correct solution-;, e., accepting all the positive examples while rejecting all the negative examples. Lots of existing works focus on XML schemas inference[16]-[18],[31], via resorting to infer regexes from examples. These approaches usually aim to tackle restricted forms of regexes from positive examples only.…”

mentioning

confidence: 99%

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

et al. 2021

2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE)

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Since regular expressions (abbrev. regexes) are dif ficult to understand and compose, automatically generating regexes has been an important research problem. This paper introduces T r a nsRe g e x , lor automatically constructing regexes from both natural language descriptions and examples. To the best of our knowledge, T r a nsRe g e x is the first to treat the Nl.P-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 T r a nsRe g e x with ten relevant state-of-theart tools on three publicly available datasets. The evaluation results demonstrate that the accuracy of our T r a nsRe g e xis 17.4%, 35.8% and 38.9% higher than that of NLP-hased approaches on the three datasets, respectively. Furthermore, T r a nsRe g e x can achieve higher accuracy than the stateof-the-art multi-modal techniques with 10% to 30% higher accuracy on all three datasets. The evaluation results also indicate

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Learning k-Occurrence Regular Expressions from Positive and Negative Samples

Mou

Chen

2019

Conceptual Modeling

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Learning k-Occurrence Regular Expressions with Interleaving

Cited by 4 publications

References 19 publications

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

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

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

Learning k-Occurrence Regular Expressions from Positive and Negative Samples

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