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ECML
2007
Springer

Statistical Debugging Using Latent Topic Models

9 years 3 months ago
Statistical Debugging Using Latent Topic Models
Abstract. Statistical debugging uses machine learning to model program failures and help identify root causes of bugs. We approach this task using a novel Delta-Latent-Dirichlet-Allocation model. We model execution traces attributed to failed runs of a program as being generated by two types of latent topics: normal usage topics and bug topics. Execution traces attributed to successful runs of the same program, however, are modeled by usage topics only. Joint modeling of both kinds of traces allows us to identify weak bug topics that would otherwise remain undetected. We perform model inference with collapsed Gibbs sampling. In quantitative evaluations on four real programs, our model produces bug topics highly correlated to the true bugs, as measured by the Rand index. Qualitative evaluation by domain experts suggests that our model outperforms existing statistical methods for bug cause identiļ¬cation, and may help support other software tasks not addressed by earlier models.
David Andrzejewski, Anne Mulhern, Ben Liblit, Xiao
Added 07 Jun 2010
Updated 07 Jun 2010
Type Conference
Year 2007
Where ECML
Authors David Andrzejewski, Anne Mulhern, Ben Liblit, Xiaojin Zhu
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