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ICSE 2021
Mon 17 May - Sat 5 June 2021

In the last decade, two paradigm shifts have reshaped the software industry - the move from boxed products to services and the widespread adoption of cloud computing. This has had a huge impact on the software development life cycle and the DevOps processes. Particularly, incident management has become critical for developing and operating large-scale services. Incidents are created to ensure timely communication of service issues and, also, their resolution. Prior work on incident management has been heavily focused on the challenges with incident triaging and de-duplication. In this work, we address the fundamental problem of structured knowledge extraction from service incidents. We have built SoftNER, a framework for unsupervised knowledge extraction from service incidents. We frame the knowledge extraction problem as a Named-entity Recognition task for extracting factual information. SoftNER leverages structural patterns like key,value pairs and tables for bootstrapping the training data. Further, we build a novel multi-task learning based BiLSTM-CRF model which leverages not just the semantic context but also the data-types for named-entity extraction. We have deployed SoftNER at Microsoft, a major cloud service provider and have evaluated it on more than 2 months of cloud incidents. We show that the unsupervised machine learning based approach has a high precision of 0.96. Our multi-task learning based deep learning model also outperforms the state of the art NER models. Lastly, using the knowledge extracted by SoftNER we are able to build significantly more accurate models for important downstream tasks like incident triaging.

Conference Day
Thu 27 May

Displayed time zone: Amsterdam, Berlin, Bern, Rome, Stockholm, Vienna change

15:05 - 16:05
3.3.1. Monitoring Cloud-Based ServicesTechnical Track / SEIP - Software Engineering in Practice at Blended Sessions Room 1 +12h
Chair(s): Andrea ZismanThe Open University
15:05
20m
Paper
Fast Outage Analysis of Large-scale Production Clouds with Service Correlation MiningTechnical Track
Technical Track
Yaohui WangFudan University, Guozheng LiPeking University, Zijian WangFudan University, Yu KangMicrosoft Research, Beijing, China, Yangfan ZhouFudan University, Hongyu ZhangThe University of Newcastle, Feng GaoMicrosoft Azure, Jeffrey SunMicrosoft Azure, Li YangMicrosoft Azure, Pochian LeeMicrosoft Azure, Zhangwei XuMicrosoft Azure, Pu ZhaoMicrosoft Research, Beijing, China, Bo QiaoMicrosoft Research, Beijing, China, Liqun LiMicrosoft Research, Beijing, China, Xu ZhangMicrosoft Research, Beijing, China, Qingwei LinMicrosoft Research, Beijing, China
Pre-print Media Attached
15:25
20m
Paper
Neural Knowledge Extraction From Cloud Service IncidentsSEIP
SEIP - Software Engineering in Practice
Manish ShettyMicrosoft Research, India, Chetan BansalMicrosoft Research, Sumit KumarMicrosoft, Nikitha RaoMicrosoft Research, Nachiappan NagappanMicrosoft Research, Thomas ZimmermannMicrosoft Research
Link to publication DOI Pre-print Media Attached
15:45
20m
Paper
FIXME: Enhance Software Reliability with Hybrid Approaches in CloudSEIP
SEIP - Software Engineering in Practice
Jinho HwangIBM Research, Larisa ShwartzIBM, Qing WangInstitute of Software, Chinese Academy of Sciences, Raghav BattaIBM, Harshit KumarIBM, Michael NiddIBM
Pre-print Media Attached

Conference Day
Fri 28 May

Displayed time zone: Amsterdam, Berlin, Bern, Rome, Stockholm, Vienna change

03:05 - 04:05
03:05
20m
Paper
Fast Outage Analysis of Large-scale Production Clouds with Service Correlation MiningTechnical Track
Technical Track
Yaohui WangFudan University, Guozheng LiPeking University, Zijian WangFudan University, Yu KangMicrosoft Research, Beijing, China, Yangfan ZhouFudan University, Hongyu ZhangThe University of Newcastle, Feng GaoMicrosoft Azure, Jeffrey SunMicrosoft Azure, Li YangMicrosoft Azure, Pochian LeeMicrosoft Azure, Zhangwei XuMicrosoft Azure, Pu ZhaoMicrosoft Research, Beijing, China, Bo QiaoMicrosoft Research, Beijing, China, Liqun LiMicrosoft Research, Beijing, China, Xu ZhangMicrosoft Research, Beijing, China, Qingwei LinMicrosoft Research, Beijing, China
Pre-print Media Attached
03:25
20m
Paper
Neural Knowledge Extraction From Cloud Service IncidentsSEIP
SEIP - Software Engineering in Practice
Manish ShettyMicrosoft Research, India, Chetan BansalMicrosoft Research, Sumit KumarMicrosoft, Nikitha RaoMicrosoft Research, Nachiappan NagappanMicrosoft Research, Thomas ZimmermannMicrosoft Research
Link to publication DOI Pre-print Media Attached
03:45
20m
Paper
FIXME: Enhance Software Reliability with Hybrid Approaches in CloudSEIP
SEIP - Software Engineering in Practice
Jinho HwangIBM Research, Larisa ShwartzIBM, Qing WangInstitute of Software, Chinese Academy of Sciences, Raghav BattaIBM, Harshit KumarIBM, Michael NiddIBM
Pre-print Media Attached