Automatic Localization of Bugs to Faulty Components in Large Scale Software Systems Using Bayesian Classification

Abstract

We suggest a Bayesian approach to the problem of reducing bug turn-around time in large software development organizations. Our approach is to use classification to predict where bugs are located in components. This classification is a form of automatic fault localization (AFL) at the component level. The approach only relies on historical bug reports and does not require detailed analysis of source code or detailed test runs. Our approach addresses two problems identified in user studies of AFL tools. The first problem concerns the trust in which the user can put in the results of the tool. The second problem concerns understanding how the results were computed. The proposed model quantifies the uncertainty in its predictions and all estimated model parameters. Additionally, the output of the model explains why a result was suggested. We evaluate the approach on more than 50000 bugs.

DOI: 10.1109/QRS.2016.54

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Cite this paper

@article{Jonsson2016AutomaticLO, title={Automatic Localization of Bugs to Faulty Components in Large Scale Software Systems Using Bayesian Classification}, author={Leif Jonsson and David Broman and M{\aa}ns Magnusson and Kristian Sandahl and Mattias Villani and Sigrid Eldh}, journal={2016 IEEE International Conference on Software Quality, Reliability and Security (QRS)}, year={2016}, pages={423-430} }