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Goal: We consider the problem of automatically grouping logs of runs thatfailed for the same underlying reasons, so that they can be treated moreeffectively, and investigate the following questions: (1) Does an approachdeveloped to identify problems in system logs generalize to identifyingproblems in continuous deployment logs? (2) How does dimensionality reductionaffect the quality of automated log clustering? (3) How does the criterion usedfor merging clusters in the clustering algorithm affect clustering quality?  Method: We replicate and extend earlier work on clustering system log filesto assess its generalization to continuous deployment logs. We consider theoptional inclusion of one of these dimensionality reduction techniques:Principal Component Analysis (PCA), Latent Semantic Indexing (LSI), andNon-negative Matrix Factorization (NMF). Moreover, we consider threealternative cluster merge criteria (Single Linkage, Average Linkage, andWeighted Linkage), in addition to the Complete Linkage criterion used inearlier work. We empirically evaluate the 16 resulting configurations oncontinuous deployment logs provided by our industrial collaborator.  Results: Our study shows that (1) identifying problems in continuousdeployment logs via clustering is feasible, (2) including NMF significantlyimproves overall accuracy and robustness, and (3) Complete Linkage performsbest of all merge criteria analyzed.  Conclusions: We conclude that problem identification via automated logclustering is improved by including dimensionality reduction, as it decreasesthe pipeline's sensitivity to parameter choice, thereby increasing itsrobustness for handling different inputs.

Improving Problem Identification via Automated Log Clustering using Dimensionality Reduction