Page 182 - 《软件学报》2026年第2期

余广坝 等: 面向大规模在线系统的故障根因变更识别 661

166–182. [doi: 10.1007/s10742-014-0123-z]
[24] Chen PF, Qi Y, Zheng PF, Hou D. CauseInfer: Automatic and distributed performance diagnosis with hierarchical causality graph in large
distributed systems. In: Proc. of the 2014 IEEE Conf. on Computer Communications. Toronto: IEEE, 2014. 1887–1895. [doi: 10.1109/
INFOCOM.2014.6848128]
[25] Kim M, Sumbaly R, Shah S. Root cause detection in a service-oriented architecture. In: Proc. of the 2013 ACM SIGMETRICS/Int’l Conf.
on Measurement and Modeling of Computer Systems. Pittsburgh: ACM, 2013. 93–104. [doi: 10.1145/2465529.2465753]
[26] Ma M, Xu JM, Wang Y, Chen PF, Zhang ZH, Wang P. AutoMAP: Diagnose your microservice-based Web applications automatically.
In: Proc. of the 2020 Web Conf. Taipei: ACM, 2020. 246–258. [doi: 10.1145/3366423.3380111]
[27] Lin JJ, Chen PF, Zheng ZB. Microscope: Pinpoint performance issues with causal graphs in micro-service environments. In: Proc. of the
16th Int’l Conf. on Service-oriented Computing. Hangzhou: Springer, 2018. 3–20. [doi: 10.1007/978-3-030-03596-9_1]
[28] Bhagwan R, Kumar R, Ramjee R, Varghese G, Mohapatra S, Manoharan H, Shah P. Adtributor: Revenue debugging in advertising
systems. In: Proc. of the 11th USENIX Symp. on Networked Systems Design and Implementation (NSDI 2014). Seattle: USENIX
Association, 2014. 43–55.
[29] Lin QW, Lou JG, Zhang HY, Zhang DM. iDice: Problem identification for emerging issues. In: Proc. of the 38th IEEE/ACM Int’l Conf.
on Software Engineering (ICSE). Austin: IEEE, 2016. 214–224. [doi: 10.1145/2884781.2884795]
[30] Wang H, Rong GP, Xu YC, You Y. ImpAPTr: A tool for identifying the clues to online service anomalies. In: Proc. of the 35th
IEEE/ACM Int’l Conf. on Automated Software Engineering (ASE). Melbourne: IEEE, 2020. 1307–1311.
[31] Li LQ, Zhang X, He SL, Kang Y, Zhang HY, Ma MH, Dang YN, Xu ZW, Rajmohan S, Lin QW, Zhang DM. CONAN: Diagnosing batch
failures for cloud systems. In: Proc. of the 45th IEEE/ACM Int’l Conf. on Software Engineering: Software Engineering in Practice (ICSE-
SEIP). Melbourne: IEEE, 2023. 138–149. [doi: 10.1109/ICSE-SEIP58684.2023.00018]
[32] Guo XF, Peng X, Wang HZ, Li WX, Jiang H, Ding D, Xie T, Su LF. Graph-based trace analysis for microservice architecture
understanding and problem diagnosis. In: Proc. of the 28th ACM Joint Meeting on European Software Engineering Conf. and Symp. on
the Foundations of Software Engineering. ACM, 2020. 1387–1397. [doi: 10.1145/3368089.3417066]
[33] Kaldor J, Mace J, Bejda M, Gao E, Kuropatwa W, O'Neill J, Ong KW, Schaller B, Shan PJ, Viscomi B, Venkataraman V, Veeraraghavan
K, Song YJ. Canopy: An end-to-end performance tracing and analysis system. In: Proc. of the 26th Symp. on Operating Systems
Principles. Shanghai: ACM, 2017. 34–50. [doi: 10.1145/3132747.3132749]
[34] Viera AJ, Garrett JM. Understanding interobserver agreement: The Kappa statistic. Family Medicine, 2005, 37(5): 360–363.
[35] He ZL, Chen PF, Luo Y, Yan QY, Chen HY, Yu GB, Li FY. Graph based incident extraction and diagnosis in large-scale online systems.
In: Proc. of the 37th IEEE/ACM Int’l Conf. on Automated Software Engineering. Rochester: ACM, 2023. 48. [doi: 10.1145/3551349.
3556904]
[36] Kohavi R, Deng A, Frasca B, Longbotham R, Walker T, Xu Y. Trustworthy online controlled experiments: Five puzzling outcomes
explained. In: Proc. of the 18th SIGKDD Int’l Conf. on Knowledge Discovery and Data Mining. Beijing: ACM, 2012. 786–794. [doi: 10.
1145/2339530.2339653]
[37] Zhou X, Peng X, Xie T, Sun J, Ji C, Li WH, Ding D. Fault analysis and debugging of microservice systems: Industrial survey, benchmark
system, and empirical study. IEEE Trans. on Software Engineering, 2021, 47(2): 243–260. [doi: 10.1109/TSE.2018.2887384]
[38] Cotroneo D, De Simone L, Liguori P, Natella R, Bidokhti N. How bad can a bug get? An empirical analysis of software failures in the
Openstack cloud computing platform. In: Proc. of the 27th ACM Joint Meeting on European Software Engineering Conf. and Symp. on
the Foundations of Software Engineering. Tallinn: ACM, 2019. 200–211. [doi: 10.1145/3338906.3338916]

附中文参考文献
[18] 蔡亮, 范元瑞, 鄢萌, 夏鑫. 即时软件缺陷预测研究进展. 软件学报, 2019, 30(5): 1288–1307. http://www.jos.org.cn/1000-9825/5713.
htm [doi: 10.13328/j.cnki.jos.005713]
[20] 张莉, 钱冠群, 李琳. 基于变更传播仿真的软件稳定性分析. 计算机学报, 2010, 33(3): 440–451. [doi: 10.3724/SP.J.1016.2010.00440]

作者简介
余广坝, 博士, CCF 专业会员, 主要研究领域为云计算, 分布式系统可靠性.
陈鹏飞, 博士, 教授, 博士生导师, 主要研究领域为分布式系统, 云计算, AIOps.
唐锡涛, 硕士生, 主要研究领域为分布式系统, 云计算.
郑子彬, 博士, 教授, 博士生导师, CCF 杰出会员, 主要研究领域为区块链, 服务计算, 软件可靠性.