Modeling changeset topics for feature location

Abstract

Feature location is a program comprehension activity in which a developer inspects source code to locate the classes or methods that implement a feature of interest. Many feature location techniques (FLTs) are based on text retrieval models, and in such FLTs it is typical for the models to be trained on source code snapshots. However, source code evolution leads to model obsolescence and thus to the need to retrain the model from the latest snapshot. In this paper, we introduce a topic-modeling-based FLT in which the model is built incrementally from source code history. By training an online learning algorithm using changesets, the FLT maintains an up-to-date model without incurring the non-trivial computational cost associated with retraining traditional FLTs. Overall, we studied over 600 defects and features from 4 open-source Java projects. We also present a historical simulation that demonstrates how the FLT performs as a project evolves. Our results indicate that the accuracy of a changeset-based FLT is similar to that of a snapshot-based FLT, but without the retraining costs.