Predicting Bug Fix Time: Using Standard Versus Topic Based Text Categorization Techniques
In modern software development, finding and fixing bugs is a vital part of software development and quality assurance. Once a bug is reported, it is typically recorded in the Bug Tracking System, and is assigned to a developer to resolve (bug triage) Current practice of bug triage is largely a manual collaborative process, which is often time consuming and error prone. Predicting on the basis of past data the time to fix a newly reported bug has been shown to be an important target to support the whole triage process. Many researchers have, therefore, proposed methods for automated bug fix time prediction, largely based on statistical prediction models exploiting the attributes of bug reports. However, existing algorithms often fail to validate on multiple large projects widely used in bug studies, mostly as a consequence of inappropriate attribute selection [2] In this paper, instead of focusing on attribute subset selection, we explore an alternative promising approach consisting of using all available textual information. The problem of bug fix time estimation is then mapped to a text categorization problem. We consider a multi topic Supervised Latent Dirichlet Allocation (SLDA) model, which adds to Latent Dirichlet Allocation a response variable consisting of an unordered binary target variable, denoting time to resolution discretized into FAST (negative class) and SLOW (positive class) labels. We have evaluated SLDA on four large scale open source projects. We show that the proposed model greatly improves recall, when compared to standard single topic algorithms.
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