Comparing Incremental Latent Semantic Analysis Algorithms for Efficient Retrieval from Software Libraries for Bug Localization

The problem of bug localization is to identify the source files related to a bug in a software repository. Information Retrieval (IR) based approaches create an index of the source files and learn a model which is then queried with a bug for the relevant files. In spite of the advances in these tools, the current approaches do not take into consideration the dynamic nature of software repositories. With the traditional IR based approaches to bug localization, the model parameters must be recalculated for each change to a repository. In contrast, this paper presents an incremental framework to update the model parameters of the Latent Semantic Analysis (LSA) model as the data evolves. We compare two state-of-the-art incremental SVD update techniques for LSA with respect to the retrieval accuracy and the time performance. The dataset we used in our validation experiments was created from mining 10 years of version history of AspectJ and JodaTime software libraries.

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