An empirical dataset of regular expression-related bugs from pull requests, created for the 'An Empirical Study on Regular Expression Bugs' MSR submission. It includes root causes, manifestations, categories, fixes, and metrics. The dataset was authored by Chris Brown and is available via the paperswithcode platform under an Open Access (green) license.
Use Cases
- Analyzing root causes of regular expression bugs based on the described categorization.
- Studying patterns in bug manifestations and fixes mentioned in the description.
- Training models to classify or predict regular expression bug types based on the provided metrics.
Strengths
- Dataset is structured for an empirical study, suggesting a research-grade collection.
- Includes multiple analysis dimensions such as root causes, manifestations, and fixes.
Limitations
- Column-level documentation is absent; field semantics must be inferred after download.
- Row count is unknown, which may limit suitability assessment.
- Last update date is unknown; freshness unverified.
Provenance
- Source
- paperswithcode
- Collection Method
- Likely extracted from software repository pull requests for an empirical study.