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                     with extended spectrum analysis in microservice environments. In: Proc. of the 2021 Web Conf. Ljubljana: ACM, 2021. 3087–3098.
                  [5]   Yu GB, Chen PF, Li YF, Chen HY, Li XY, Zheng ZB. Nezha: Interpretable fine-grained root causes analysis for microservices on multi-
                     modal  observability  data.  In:  Proc.  of  the  31st  ACM  Joint  European  Software  Engineering  Conf.  and  Symp.  on  the  Foundations  of
                     Software Engineering (FSE). San Francisco: ACM, 2023. 553–565. [doi: 10.1145/3611643.3616249]
                  [6]   Yu  GB,  Huang  ZC,  Chen  PF.  TraceRank:  Abnormal  service  localization  with  dis-aggregated  end-to-end  tracing  data  in  cloud  native
                     systems. Journal of Software: Evolution and Process, 2023, 35(10): e2413. [doi: 10.1002/smr.2413]
                  [7]   Li YF, Yu GB, Chen PF, Zhang CF, Zheng ZB. MicroSketch: Lightweight and adaptive sketch based performance issue detection and
                     localization in microservice systems. In: Proc. of the 20th Int’l Conf. on Service-oriented Computing. Seville: Springer, 2022. 219–236.
                     [doi: 10.1007/978-3-031-20984-0_15]
                  [8]   Batta R, Shwartz L, Nidd M, Azad AP, Kumar H. A system for proactive risk assessment of application changes in cloud operations. In:
                     Proc. of the 14th IEEE Int’l Conf. on Cloud Computing (CLOUD). Chicago: IEEE, 2021. 112–123. [doi: 10.1109/CLOUD53861.2021.
                     00025]
                  [9]   Zhang SL, Liu Y, Pei D, Chen Y, Qu XP, Tao SM, Zang Z. Rapid and robust impact assessment of software changes in large Internet-
                     based services. In: Proc. of the 11th Conf. on Emerging Networking Experiments and Technologies. Heidelberg: ACM, 2015. 2. [doi: 10.
                     1145/2716281.2836087]
                 [10]   Xu TY, Jin XX, Huang P, Zhou YY, Lu S, Jin L, Pasupathy S. Early detection of configuration errors to reduce failure damage. In: Proc.
                     of the 12th USENIX Symp. on Operating Systems Design and Implementation (OSDI). Savannah: USENIX Association, 2016. 619–634.
                 [11]   Li Z, Cheng Q, Hsieh K, Dang YN, Huang P, Singh P, Yang XS, Lin QW, Wu YJ, Levy S, Chintalapati M. Gandalf: An intelligent, end-
                     to-end analytics service for safe deployment in cloud-scale infrastructure. In: Proc. of the 17th USENIX Symp. on Networked Systems
                     Design and Implementation (NSDI 2020). Santa Clara: USENIX Association, 2020. 389–402.
                 [12]   Zhao  NW,  Chen  JJ,  Yu  ZY,  Wang  HL,  Li  JS,  Qiu  B,  Xu  HY,  Zhang  WC,  Sui  KX,  Pei  D.  Identifying  bad  software  changes  via
                     multimodal anomaly detection for online service systems. In: Proc. of the 29th Joint Meeting on European Software Engineering Conf.
                     and Symp. on the Foundations of Software Engineering. Athens: ACM, 2021. 527–539. [doi: 10.1145/3468264.3468543]
                 [13]   Zhang YL, Yang JW, Jin ZQ, Sethi U, Rodrigues K, Lu S, Yuan D. Understanding and detecting software upgrade failures in distributed
                     systems. In: Proc. of the 28th ACM SIGOPS Symp. on Operating Systems Principles. ACM, 2021. 116–131. [doi: 10.1145/3477132.
                     3483577]
                 [14]   Dumitraş T, Narasimhan P. Why do upgrades fail and what can we do about it? Toward dependable, online upgrades in enterprise system.
                     In: Proc. of the 10th Int’l Conf. on Middleware. Urbana: Springer, 2009. 349–372. [doi: 10.1007/978-3-642-10445-9_18]
                 [15]   Li XY, Yu GB, Chen PF, Chen HY, Chen ZK. Going through the life cycle of faults in clouds: Guidelines on fault handling. In: Proc. of
                     the 33rd IEEE Int’l Symp. on Software Reliability Engineering (ISSRE). Charlotte: IEEE, 2022. 121–132. [doi: 10.1109/ISSRE55969.
                     2022.00022]
                 [16]   Zhang C, Chen BH, Peng X, Zhao WY. BuildSheriff: Change-aware test failure triage for continuous integration builds. In: Proc. of the
                     44th IEEE/ACM Int’l Conf. on Software Engineering (ICSE). Pittsburgh: IEEE, 2022. 312–324. [doi: 10.1145/3510003.3510132]
                 [17]   Küçük  Y,  Henderson  TAD,  Podgurski  A.  Improving  fault  localization  by  integrating  value  and  predicate  based  causal  inference
                     techniques. In: Proc. of the 43rd IEEE/ACM Int’l Conf. on Software Engineering (ICSE). Madrid: IEEE, 2021. 649–660. [doi: 10.1109/
                     ICSE43902.2021.00066]
                 [18]   Cai L, Fan YR, Yan M, Xia X. Just-in-time software defect prediction: Literature review. Ruan Jian Xue Bao/Journal of Software, 2019,
                     30(5): 1288–1307 (in Chinese with English abstract). http://www.jos.org.cn/1000-9825/5713.htm [doi: 10.13328/j.cnki.jos.005713]
                 [19]   Mehta  S,  Bhagwan  R,  Kumar  R,  Bansal  C,  Maddila  C,  Ashok  B,  Asthana  S,  Bird  C,  Kumar  A.  Rex:  Preventing  bugs  and
                     misconfiguration in large services using correlated change analysis. In: Proc. of the 17th USENIX Symp. on Networked Systems Design
                     and Implementation. Santa Clara: USENIX Association, 2020. 435–448. [doi: 10.5555/3388242.3388274]
                 [20]   Zhang L, Qian GQ, Li L. Software stability analysis based on change impact simulation. Chinese Journal of Computers, 2010, 33(3):
                     440–451 (in Chinese with English abstract). [doi: 10.3724/SP.J.1016.2010.00440]
                 [21]   Wang XR, Yin KL, Ouyang QY, Wen XD, Zhang SL, Zhang WC, Cao L, Han JX, Jin X, Pei D. Identifying erroneous software changes
                     through  self-supervised  contrastive  learning  on  time  series  data.  In:  Proc.  of  the  33rd  IEEE  Int’l  Symp.  on  Software  Reliability
                     Engineering (ISSRE). Charlotte: IEEE, 2022. 366–377. [doi: 10.1109/ISSRE55969.2022.00043]
                 [22]   Mahimkar  A,  de  Andrade  CE,  Sinha  R,  Rana  G.  A  composition  framework  for  change  management.  In:  Proc.  of  the  2021  ACM
                     SIGCOMM Conf. ACM, 2021. 788–806. [doi: 10.1145/3452296.3472901]
                 [23]   Stuart  EA,  Huskamp  HA,  Duckworth  K,  Simmons  J,  Song  ZR,  Chernew  ME,  Barry  CL.  Using  propensity  scores  in  difference-in-
                     differences  models  to  estimate  the  effects  of  a  policy  change.  Health  Services  and  Outcomes  Research  Methodology,  2014,  14(4):
   176   177   178   179   180   181   182   183   184   185   186