This study explores the use of Code Churn and Pattern Size (PSIZ) metrics to identify bug-prone areas in Haskell codebases. The primary research questions addressed are whether these metrics can effectively predict areas of code instability and potential bugs. Our contributions include a comprehensive analysis of these metrics across three large Haskell projects, examining the correlation between high metric values and documented bugs. Our findings reveal that Code Churn is a significant indicator of potential bugs, with ’buggy’ files showing markedly higher mean code churn values. However, the PSIZ metric proved ineffective in predicting bug-prone areas, as the mean and median values for both projects and ’buggy’ files were similar and low. These results suggest that while Code Churn is a useful metric for identifying unstable code areas, PSIZ does not offer the same predictive value. Future research should expand the dataset and consider additional metrics to enhance the reliability of these findings.