LogSummary: Unstructured Log Summarization for Software Systems
Weibin Meng, Federico Zaiter, Yuzhe Zhang, Ying Liu, Shenglin Zhang, Shimin Tao, Yichen Zhu, Tao Han, Yongpeng Zhao, En Wang, Yuzhi Zhang, Dan Pei
Abstract
We propose LogSummary, an automatic, unsupervised end-to-end log summarization framework for software system maintenance in this work. LogSummary obtains the summarized triples of necessary logs for a given log sequence. It integrates a novel information extraction method that considers semantic information and domain knowledge with a new triple-ranking approach using the global knowledge learned from all logs. Given the lack of a publicly-available gold standard for log summarization, we have manually labeled the summaries of four open-source log datasets and made them publicly available. The evaluation of these datasets and the case studies on real-world logs demonstrate that LogSummary produces highly representative (average ROUGE F1 score of 0.741) summaries efficiently. We have packaged LogSummary into an open-source toolkit and hope it can be a standard baseline and benefit future log summarization works.