Using Continuous Change Analysis to Understand the Practice of Refactoring

Despite the enormous success that manual and automated refactoring has enjoyed during the last decade, we know little about the practice of refactoring. Understanding the refactoring practice is important for developers, refactoring tool builders, and researchers. Many previous approaches to study refactorings are based on comparing code snapshots, which is imprecise, incomplete, and does not allow to answer research questions that involve time or compare manual and automated refactoring. We present the first empirical study that considers both manual and automated refactoring. This study is enabled by our novel algorithm, which infers refactorings from continuous changes. We applied this algorithm to the code evolution data collected from 23 developers working in their natural environment for 1,520 hours. Using a corpus of 5,269 refactorings, we reveal several surprising facts about how manual and automated refactorings are different. For example, some popular automated refactorings are not representative when taking into account manual refactorings. More than one third of the refactorings performed by developers are grouped. For some refactoring kinds, up to 42% of performed refactorings do not reach the Version Control System.