Incorporating version histories in Information Retrieval based bug localization

Abstract

Fast and accurate localization of software defects continues to be a difficult problem since defects can emanate from a large variety of sources and can often be intricate in nature. In this paper, we show how version histories of a software project can be used to estimate a prior probability distribution for defect proneness associated with the files in a given version of the project. Subsequently, these priors are used in an IR (Information Retrieval) framework to determine the posterior probability of a file being the cause of a bug. We first present two models to estimate the priors, one from the defect histories and the other from the modification histories, with both types of histories as stored in the versioning tools. Referring to these as the base models, we then extend them by incorporating a temporal decay into the estimation of the priors. We show that by just including the base models, the mean average precision (MAP) for bug localization improves by as much as 30%. And when we also factor in the time decay in the estimates of the priors, the improvements in MAP can be as large as 80%.