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ASE 2021
Sun 14 - Sat 20 November 2021 Australia
Wed 17 Nov 2021 11:00 - 11:20 at Kangaroo - Finding Defects Chair(s): Xiao Liu

As online service systems continue to grow in terms of complexity and volume, how service incidents are managed will greatly impact company revenue and user trust. Due to the cascading effect, cloud failure often comes with an overwhelming number of incidents from dependent services and devices. To pursue an efficient incident management, related incidents should be quickly aggregated to narrow down the problem scope. To this end, in this paper, we propose GRLIA, an incident aggregation framework based on graph representation learning over a graph of cascaded cloud failures. The graph representation is learned for each unique incident in an unsupervised and unified fashion to simultaneously encode the topological and temporal relationship among incidents. Therefore, it can be easily employed for online incident aggregation by measuring their distance. Furthermore, we leverage fine-grained system monitoring data, i.e., Key Performance Indicators (KPIs), to identify the complete scope of failures’ cascading impact. The proposed framework is evaluated with real-world incident data collected from a large-scale online service system of company $\mathcal{H}$. The experimental results demonstrate that GRLIA is effective and outperforms existing methods. Furthermore, our framework has been successfully deployed in industrial practice.

Wed 17 Nov

Displayed time zone: Hobart change

11:00 - 12:00
Finding DefectsResearch Papers / NIER track / Journal-first Papers at Kangaroo
Chair(s): Xiao Liu School of Information Technology, Deakin University
Graph-based Incident Aggregation for Large-Scale Online Service Systems
Research Papers
Zhuangbin Chen Chinese University of Hong Kong, China, Yuxin Su The Chinese University of Hong Kong, Jinyang Liu , Hongyu Zhang University of Newcastle, Xuemin Wen Huawei Technologies, Xiao Ling Huawei Technologies, Yongqiang Yang Huawei Technologies, Michael Lyu The Chinese University of Hong Kong
PyExplainer: Explaining the Predictions of Just-In-Time Defect ModelsACM Distinguished Paper
Research Papers
Chanathip Pornprasit Monash University, Kla Tantithamthavorn Monash University, Jirayus Jiarpakdee Monash University, Australia, Michael Fu Monash University, Patanamon Thongtanunam University of Melbourne
Towards Systematic and Dynamic Task Allocation for Collaborative Parallel Fuzzing
NIER track
Thuan Pham The University of Melbourne, Manh-Dung Nguyen Montimage R&D, France, Quang-Trung Ta National University of Singapore, Toby Murray University of Melbourne, Benjamin I.P. Rubinstein University of Melbourne
An Extensive Study on Smell-Aware Bug Localization
Journal-first Papers
Aoi Takahashi Tokyo Institute of Technology, Natthawute Sae-Lim Tokyo Institute of Technology, Shinpei Hayashi Tokyo Institute of Technology, Motoshi Saeki Nanzan University
Link to publication DOI