title: SplitRegex: Faster Regex Synthesis via Neural Example Splitting
abstract: Due to the practical importance of regular expressions (regexes, for short), there has been a lot of research to automatically generate regexes from positive and negative string examples. A basic idea of learning a regex is a search-and-repair; search for a correct regex and repair it if incorrect. The problem is known to be PSPACE-complete and the main issue is to obtain a regex quickly within a time limit. 
  While classical regex learning methods do not perform well, recent approaches using deep neural networks show better performance
  with respect to the accuracy of the resulting regexes. However, all these approaches including SOTA models are often extremely slow because of the slow searching mechanism, and do not produce desired regexes within a given time limit. 
  We tackle the problem of learning regexes faster from positive and negative strings by relying on a novel approach called `neural example splitting'. Our approach essentially split up example strings into multiple parts using a neural network trained to group similar substrings from positive strings. This helps to learn a regex faster and, thus, more accurately since we now learn from several short-length strings.
  We propose an effective regex synthesis framework called `SplitRegex' that synthesizes subregexes from `split' positive substrings and produces the final regex by concatenating the synthesized subregexes. For the negative sample, we exploit pre-generated subregexes during the subregex synthesis process and perform the matching against negative strings. Then the final regex becomes consistent with all negative strings. SplitRegex is a divided-and-conquer framework for learning target regexes; split (=divide) positive strings and infer partial regexes for multiple parts, which is much more accurate than the whole string inferring, and concatenate (=conquer) inferred regexes while satisfying negative strings. We empirically demonstrate that the proposed SplitRegex framework substantially improves the previous regex synthesis approaches over four benchmark datasets. 
authors: Su-Hyeon Kim Hyunjoon Cheon Yo-Sub Han Sang-Ki Ko
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