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Automating Root Cause Analysis via Machine Learning in Agile Software Testing Environments
2019
2019 12th IEEE Conference on Software Testing, Validation and Verification (ICST)
We apply machine learning to automate the root cause analysis in agile software testing environments. In particular, we extract relevant features from raw log data after interviewing testing engineers (human experts). Initial efforts are put into clustering the unlabeled data, and despite obtaining weak correlations between several clusters and failure root causes, the vagueness in the rest of the clusters leads to the consideration of labeling. A new round of interviews with the testing
doi:10.1109/icst.2019.00047
dblp:conf/icst/KahlesTHJ19
fatcat:fispf4ufgna6nfozetetaflxfa