Replaying IDE interactions to evaluate and improve change prediction approaches

This work proposes a benchmark for the evaluation of change prediction approaches based on fine-grained change data recorded from IDE usage and presents an evaluation procedure that is used on several change prediction approach, both novel and from the literature, and reports on the results.

Abstract

Change prediction helps developers by recommending program entities that will have to be changed alongside the entities currently being changed. To evaluate their accuracy, current change prediction approaches use data from versioning systems such as CVS or SVN. These data sources provide a coarse-grained view of the development history that flattens the sequence of changes in a single commit. They are thus not a valid basis for evaluation in the case of development-style prediction, where the order of the predictions has to match the order of the changes a developer makes. We propose a benchmark for the evaluation of change prediction approaches based on fine-grained change data recorded from IDE usage. Moreover, the change prediction approaches themselves can use the more accurate data to fine-tune their prediction. We present an evaluation procedure and use it on several change prediction approaches, both novel and from the literature, and report on the results