Preprocessing CVS Data for Fine-Grained Analysis

This document discusses preprocessing CVS data for fine-grained analysis. It describes copying CVS data into a database and inferring transactions from time windows and commit mails. Transactions are further cleaned by removing large outliers and merging related changes. Detecting fine-grained changes like functions between revisions is also discussed. Lessons learned include using sliding time windows, setting time window lengths to 3-5 minutes, and that preprocessing is key to reliable analysis.