The regular expression (regex) practices of software engineers affect the maintainability, correctness, and security of their software applications. Empirical research has described characteristics like the distribution of regex feature usage, the structural complexity of regexes, and worst-case regex match behaviors. But researchers have not critically examined the methodology they follow to extract regexes, and findings to date are typically generalized from regexes written in only 1-2 programming languages. This is an incomplete foundation. Generalizing existing research depends on validating two hypotheses: (1) Various regex extraction methodologies yield similar results, and (2) Regex characteristics are similar across programming languages. To test these hypotheses, we defined eight regex metrics to capture the dimensions of regex representation, string language diversity, and worst-case match complexity. We report that the two competing regex extraction methodologies yield comparable corpuses, suggesting that simpler regex extraction techniques will still yield sound corpuses. But in comparing regexes across programming languages, we found significant differences in some characteristics by programming language. Our findings have bearing on future empirical methodology, as the programming language should be considered, and generalizability will not be assured. Our measurements on a corpus of 537,806 regexes can guide data-driven designs of a new generation of regex tools and regex engines.

@inproceedings{paperbot5198,
  title = {Testing Regex Generalizability And Its Implications: A Large-Scale Many-Language Measurement Study},
  author = {James C Davis and Daniel Moyer and Ayaan M Kazerouni and Dongyoon Lee},
  booktitle = {ASE},
  year = {2019},
  doi = {10.1109/ase.2019.00048}
}