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Due to the practical importance of regular expressions (regexes, for short),there has been a lot of research to automatically generate regexes frompositive and negative string examples. We tackle the problem of learningregexes faster from positive and negative strings by relying on a novelapproach called `neural example splitting'. Our approach essentially split upeach example string into multiple parts using a neural network trained to groupsimilar substrings from positive strings. This helps to learn a regex fasterand, thus, more accurately since we now learn from several short-lengthstrings. We propose an effective regex synthesis framework called `SplitRegex'that synthesizes subregexes from `split' positive substrings and produces thefinal regex by concatenating the synthesized subregexes. For the negativesample, we exploit pre-generated subregexes during the subregex synthesisprocess and perform the matching against negative strings. Then the final regexbecomes consistent with all negative strings. SplitRegex is adivided-and-conquer framework for learning target regexes; split (=divide)positive strings and infer partial regexes for multiple parts, which is muchmore accurate than the whole string inferring, and concatenate (=conquer)inferred regexes while satisfying negative strings. We empirically demonstratethat the proposed SplitRegex framework substantially improves the previousregex synthesis approaches over four benchmark datasets.

Neuro-Symbolic Regex Synthesis Framework via Neural Example Splitting