Learning Tractable Probabilistic Models for Fault Localization
Authors: Aniruddh Nath, Pedro Domingos
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We performed an experiment to determine whether TFLM s ability to combine a coverage-based fault localization system with learned bug patterns improves fault localization performance, relative to using the coverage-based system directly. and Results of our experiments are displayed in tables 2 and 3, and figure 2. |
| Researcher Affiliation | Collaboration | Aniruddh Nath and Pedro Domingos Department of Computer Science & Engineering University of Washington Seattle, WA 98195, U.S.A. {nath, pedrod}@cs.washington.edu and Now at Google, Inc. |
| Pseudocode | No | The paper describes procedures and concepts textually but does not include any clearly labeled 'Pseudocode' or 'Algorithm' blocks. |
| Open Source Code | No | The paper mentions 'our unoptimized Python implementation' but does not provide a specific link or an explicit statement about releasing the source code for their methodology. |
| Open Datasets | Yes | We evaluated TFLMs on four mid-sized C programs (table 1) from the Software-artifact Infrastructure Repository (Khurshid et al. 2004). |
| Dataset Splits | Yes | For each subject program, TFLMs were learned via cross-validation, training on all versions of the program except the one being evaluated. and The number of latent subclasses was also chosen via cross-validation, from the range [1, 4]. |
| Hardware Specification | No | The paper states 'our unoptimized Python implementation predicts bug probabilities in a few seconds' but does not specify any particular hardware, such as CPU or GPU models, or memory details, used for the experiments. |
| Software Dependencies | No | The paper mentions using 'SCIKIT-LEARN (Pedregosa et al. 2011) implementation of logistic regression', but it does not provide a specific version number for scikit-learn or any other software dependencies. |
| Experiment Setup | Yes | We use the SCIKIT-LEARN (Pedregosa et al. 2011) implementation of logistic regression, with the class weight= auto parameter, to compensate for the sparsity of the buggy lines relative to bug-free lines. and For TFLMs, we ran hard EM for 100 iterations. and The number of latent subclasses was also chosen via cross-validation, from the range [1, 4]. |