  Regular expressions (regexes) are widely used in different fields of computer
science, such as programming languages, string processing, and databases.
However, existing tools for synthesizing or repairing regexes always assume
that the input examples are faultless. In real industrial scenarios, this
assumption does not entirely hold. Thus, this paper presents a simple but
effective templated-based approach to generate regular expressions over noisy
examples. Specifically, we present a data model (i.e., MetaParam) to extract
features of strings for clustering all examples. Then, we propose a practical
dynamic thresholding scheme to filter out anomalous examples via detecting knee
points on CDF graphs. Finally, we design a template-based algorithm to
translate a finite of positve examples to regular expression, which is
efficient, interpretable, and extensible. We performed an experimental
evaluation on four different extraction tasks applied to real-world datasets
and obtained promising results in terms of F-measure. Moreover, gMeta achieves
excellent results in real industrial scenarios.
