Statistical Debugging Using Latent Topic Models Export

@incollection{citeulike:2820969, 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 identification, and may help support other software tasks not addressed by earlier models.}, author = {Andrzejewski, David and Mulhern, Anne and Liblit, Ben and Zhu, Xiaojin}, citeulike-article-id = {2820969}, citeulike-linkout-0 = {http://dx.doi.org/10.1007/978-3-540-74958-5_5}, doi = {10.1007/978-3-540-74958-5_5}, journal = {Machine Learning: ECML 2007}, keywords = {debugging, markov, statistical}, pages = {6--17}, posted-at = {2008-05-21 19:06:19}, priority = {5}, title = {Statistical Debugging Using Latent Topic Models}, url = {http://dx.doi.org/10.1007/978-3-540-74958-5_5}, year = {2007} }

TY - CHAP ID - citeulike:2820969 L3 - citeulike-article-id:2820969 N2 - 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 identification, and may help support other software tasks not addressed by earlier models. JF - Machine Learning: ECML 2007 EP - 17 TI - Statistical Debugging Using Latent Topic Models SP - 6 KW - debugging KW - markov KW - statistical AU - Andrzejewski, David AU - Mulhern, Anne AU - Liblit, Ben AU - Zhu, Xiaojin PY - 2007/// UR - http://dx.doi.org/10.1007/978-3-540-74958-5_5 ER -