Log Summarisation for Defect Evolution Analysis
Rares Dolga Ran Zmigrod Rui Silva Salwa Alamir Sameena Shah
Log analysis and monitoring are essential aspects in software maintenance and
identifying defects. In particular, the temporal nature and vast size of log
data leads to an interesting and important research question: How can logs be
summarised and monitored over time? While this has been a fundamental topic of
research in the software engineering community, work has typically focused on
heuristic-, syntax-, or static-based methods. In this work, we suggest an
online semantic-based clustering approach to error logs that dynamically
updates the log clusters to enable monitoring code error life-cycles. We also
introduce a novel metric to evaluate the performance of temporal log clusters.
We test our system and evaluation metric with an industrial dataset and find
that our solution outperforms similar systems. We hope that our work encourages
further temporal exploration in defect datasets.