Identifying linked incidents in large-scale online service systems

Yujun Chen, Xian Yang, Hang Dong, Xiaoting He, Hongyu Zhang, Qingwei Lin, Junjie Chen, Pu Zhao, Yu Kang, Feng Gao

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


In large-scale online service systems, incidents occur frequently due to a variety of causes, from updates of software and hardware to changes in operation environment. These incidents could significantly degrade system’s availability and customers’ satisfaction. Some incidents are linked because they are duplicate or inter-related. The linked incidents can greatly help on-call engineers find mitigation solutions and identify the root causes. In this work, we investigate the incidents and their links in a representative real-world incident management (IcM) system. Based on the identified indicators of linked incidents, we further propose LiDAR (Linked Incident identification with DAta-driven Representation), a deep learning based approach to incident linking. More specifically, we incorporate the textual description of incidents and structural information extracted from historical linked incidents to identify possible links among a large number of incidents. To show the effectiveness of our method, we apply our method to a real-world IcM system and find that our method outperforms other state-of-the-art methods.
Original languageEnglish
Title of host publicationProceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering
Place of PublicationNew York
PublisherAssociation for Computing Machinery
Number of pages11
ISBN (Print)9781450370431
Publication statusPublished - 8 Nov 2020


  • Linked incidents
  • incident management
  • online service system


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