An incremental update framework for efficient retrieval from software libraries for bug localization

Information Retrieval (IR) based bug localization techniques use a bug reports to query a software repository to retrieve relevant source files. These techniques index the source files in the software repository and train a model which is then queried for retrieval purposes. Much of the current research is focused on improving the retrieval effectiveness of these methods. However, little consideration has been given to the efficiency of such approaches for software repositories that are constantly evolving. As the software repository evolves, the index creation and model learning have to be repeated to ensure accuracy of retrieval for each new bug. In doing so, the query latency may be unreasonably high, and also, re-computing the index and the model for files that did not change is computationally redundant. We propose an incremental update framework to continuously update the index and the model using the changes made at each commit. We demonstrate that the same retrieval accuracy can be achieved but with a fraction of the time needed by current approaches. Our results are based on two basic IR modeling techniques - Vector Space Model (VSM) and Smoothed Unigram Model (SUM). The dataset we used in our validation experiments was created by tracking commit history of AspectJ and JodaTime software libraries over a span of 10 years.