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1. Exploring Regular Expression Evolution

   https://doi.org/10.1109/SANER.2019.8667972  

   Wang, Peipei; Bai, Gina R.; Stolee, Kathryn T. (February 2019, IEEE 26th International Conference on Software Analysis, Evolution and Reengineering)

   Although there are tools to help developers understand the matching behaviors between a regular expression and a string, regular-expression related faults are still common. Learning developers’ behavior through the change history of regular expressions can identify common edit patterns, which can inform the creation of mutation and repair operators to assist with testing and fixing regular expressions. In this work, we explore how regular expressions evolve over time, focusing on the characteristics of regular expression edits, the syntactic and semantic difference of the edits, and the feature changes of edits. Our exploration uses two datasets. First, we look at GitHub projects that have a regular expression in their current version and look back through the commit logs to collect the regular expressions’ edit history. Second, we collect regular expressions composed by study participants during problem- solving tasks. Our results show that 1) 95% of the regular expressions from GitHub are not edited, 2) most edited regular expressions have a syntactic distance of 4-6 characters from their predecessors, 3) over 50% of the edits in GitHub tend to expand the scope of regular expression, and 4) the number of features used indicates the regular expression language usage increases over time. This work has implications for supporting regular expression repair and mutation to ensure test suite quality. 

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2. Exploring regular expression comprehension

   https://doi.org/10.1109/ASE.2017.8115653  

   Chapman, Carl; Wang, Peipei; Stolee, Kathryn T. (October 2017, Automated Software Engineering (ASE))
3. An Empirical Study on Regular Expression Bugs

   https://doi.org/10.1145/3379597.3387464  

   Wang, Peipei; Brown, Chris; Jennings, Jamie A.; Stolee, Kathryn T. (June 2020, International Conference on Mining Software Repositories)

   null (Ed.)
4. A Lightweight Framework for Regular Expression Verification

   Liu, Xiao; Jiang, Yufei; Wu, Dinghao (January 2019, Proceedings - IEEE International Symposium on High-Assurance Systems Engineering)
5. Data Extraction via Semantic Regular Expression Synthesis

   https://doi.org/10.1145/3622863  

   Chen, Qiaochu; Banerjee, Arko; Demiralp, Çağatay; Durrett, Greg; Dillig, Işıl (October 2023, Proceedings of the ACM on Programming Languages)

   Many data extraction tasks of practical relevance require not only syntactic pattern matching but also semantic reasoning about the content of the underlying text. While regular expressions are very well suited for tasks that require only syntactic pattern matching, they fall short for data extraction tasks that involve both a syntactic and semantic component. To address this issue, we introduce semantic regexes, a generalization of regular expressions that facilitates combined syntactic and semantic reasoning about textual data. We also propose a novel learning algorithm that can synthesize semantic regexes from a small number of positive and negative examples. Our proposed learning algorithm uses a combination of neural sketch generation and compositional type-directed synthesis for fast and effective generalization from a small number of examples. We have implemented these ideas in a new tool called Smore and evaluated it on representative data extraction tasks involving several textual datasets. Our evaluation shows that semantic regexes can better support complex data extraction tasks than standard regular expressions and that our learning algorithm significantly outperforms existing tools, including state-of-the-art neural networks and program synthesis tools. 

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