@misc{15637,
  keywords = {Continuous Engineering, Log Clustering, Log Diagnosis. Effort Reduction},
  author = {Leon Moonen},
  title = {Supporting Continuous Engineering 
with Automated Log Clustering and Diagnosis},
  abstract = {Abstract: Continuous engineering (CE) practices, such as continuous integration and continuous deployment,have become key to modern software development. They are characterized by short automated build and test cycles that give developers early feedback on potential issues. CE practices help to release software more frequently, and reduces risk by increasing incrementality. However, effective use of CE practices in industrial projects requires making sense of the vast amounts of data that results from the repeated build and test cycles.In this talk, we will explore to what extent these data can be treated more effectively by automatically grouping logs of runs that failed for the same underlying reasons, and what effort reduction can be achieved. Our proposed approach builds on earlier work for system log clustering, and we empirically investigate how choices in the clustering pipeline affect the grouping of continuous deployment logs provided by our industrial collaborator. We conclude by discussing a log diagnosis technique that builds on event spectra can be used to highlight which events in a failure log are most likely to indicate the causes of the failure.},
  year = {2019},
  journal = {KTH Royal Institute of Technology, Stockholm, Sweden},
}