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These are the datasets for our ESEC/FSE'22 paper "Actionable and Interpretable Fault Localization for Recurring Failures in Online Service Systems." In each dataset, graph.yml or graphs/*.yml are FDGs, metrics.csv is metrics, and faults.csv is failures (including ground truths).FDG.pkl is a pickle of the FDG object, which contains all the above data. Note that the pickle files are not compatible in different Python and Pandas versions. So if you cannot load the pickles, just ignore and delete them. They are only used to speed up data load. See more at https://github.com/NetManAIOps/DejaVu
failure diagnosis, online service system, metric, trace, time series, root cause localization, fault localization
failure diagnosis, online service system, metric, trace, time series, root cause localization, fault localization
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 2 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
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| downloads | 117 |

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