William P, John S, Tony E, Andriy M. On challenges of cloud monitoring. In Proceedings of the 27th Annual International Conference on Computer Science and Software Engineering, CASCON ’17, page 259–265, USA, 2017. IBM Corp.
Ståhl D, Mårtensson T, Bosch J. Continuous practices and devops: Beyond the buzz, what does it all mean? In 2017 43rd Euromicro Conference on Software Engineering and Advanced Applications (SEAA), pages 440–448, Vienna, 2017. IEEE.
Capizzi A, Distefano S, Mazzara M. From DevOps to DevDataOps: Data management in devops processes. In Jean-Michel B, Manuel M, Bertrand M, editors, Software Engineering Aspects of Continuous Development and New Paradigms of Software Production and Deployment, pages 52–62. Springer, New York, 2020.
Fitzgerald B, Stol K-J. Continuous software engineering: a roadmap and agenda. J Syst Softw. 2017;123:176–89.
Felderer M, Russo B, Auer F. On testing data-intensive software systems. In Stefan B, Matthias E, Arndt L, Edgar RW, editors, Security and Quality in Cyber-Physical Systems Engineering, With Forewords by Robert M. Lee and Tom Gilb, pages 129–148. Springer, 2019.
Zhao N, Chen J, Peng X, Wang H, Wu X, Zhang Y, Chen Z, Zheng X, Nie X, Wang G, Wu Y, Zhou F, Zhang W, Sui K, Pei D. Understanding and handling alert storm for online service systems. In 2020 IEEE/ACM 42nd International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP), pages 162–171, 2020.
Runeson P, Engström E, Storey M-A. The design science paradigm as a frame for empirical software engineering. In Michael F, Guilherme HT, editors, Contemporary Empirical Methods in Software Engineering, pages 127–147. Springer, 2020.
Dang Y, Lin Q, Huang P. AIOps: Real-World Challenges and Research Innovations. In 2019 IEEE/ACM 41st International Conference on Software Engineering: Companion Proceedings (ICSE-Companion), pages 4–5, Montreal, QC, Canada, May 2019. IEEE.
Laukkanen E, Itkonen J, Lassenius C. Problems, causes and solutions when adopting continuous delivery—A systematic literature review. Inf Softw Technol. 2017;82:55–79.
Pilar R, Alireza H, Lucy EL, Susanna T, Tanja S, Juho E, Teemu K, Pasi K, June MV, Markku O. Continuous deployment of software intensive products and services: a systematic mapping study. J Syst Softw. 2017;123:263–91.
Mojtaba S, Muhammad AB, Liming Z. Continuous integration, delivery and deployment: a systematic review on approaches, tools, challenges and practices. IEEE Access. 2017;5:3909–43.
Mishra A, Otaiwi Z. Devops and software quality: a systematic mapping. Comput Sci Rev. 2020;38:100308.
Suonsyrjä S, Hokkanen L, Terho H, Systä K, Mikkonen T. Post-deployment data: A recipe for satisfying knowledge needs in software development? In IWSM-MENSURA, pages 139–147. IEEE, 2016.
Alessandro O, Donglin L, Mary JH, Richard L. Gamma system: continuous evolution of software after deployment. SIGSOFT Softw Eng Notes. 2002;27(4):65–9.
Pietrantuono R, Bertolino A, De Angelis G, Miranda B, Russo S. Towards Continuous Software Reliability Testing in DevOps. In 2019 IEEE/ACM 14th International Workshop on Automation of Software Test (AST), pages 21–27, Montreal, QC, Canada, May 2019. IEEE.
Xu X, Zhu L, Fu M, Sun D, Binh Tran A, Rimba P, Dwarakanathan S, Bass L. Crying wolf and meaning it: Reducing false alarms in monitoring of sporadic operations through pod-monitor. 2015 IEEE/ACM 1st International Workshop on Complex Faults & Failures in Large Software Systems (COUFLESS), pages 69 – 75, 2015.
Zhao N, Jin P, Wang L, Yang X, Liu R, Zhang W, Sui K, Pei D. Automatically and Adaptively Identifying Severe Alerts for Online Service Systems. In IEEE INFOCOM 2020 - IEEE Conference on Computer Communications, pages 2420–2429, Toronto, ON, Canada, July 2020. IEEE.
Cito J, Wettinger J, Lwakatare LE, Borg M, Li F. Feedback from operations to software development–a DevOps perspective on runtime metrics and logs. In Jean-Michel Bruel, Manuel Mazzara, and Bertrand Meyer, editors, Software Engineering Aspects of Continuous Development and New Paradigms of Software Production and Deployment, pages 184–195. Springer International Publishing, 2019.
Capizzi A, Distefano S, Araújo LJP, Mazzara M, Ahmad M, Bobrov E. Anomaly detection in DevOps Toolchain. In Jean-Michel Bruel, Manuel Mazzara, and Bertrand Meyer, editors, Software Engineering Aspects of Continuous Development and New Paradigms of Software Production and Deployment, pages 37–51. Springer International Publishing, 2020.
Du M, Li F, Zheng G, Srikumar V. DeepLog: Anomaly Detection and Diagnosis from System Logs through Deep Learning. In Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security, pages 1285–1298, Dallas Texas USA, October 2017. ACM.
He S, Zhu J, He P, Lyu MR. Experience Report: System Log Analysis for Anomaly Detection. In 2016 IEEE 27th International Symposium on Software Reliability Engineering (ISSRE), pages 207–218, Ottawa, ON, Canada, October 2016. IEEE.
Fu X, Ren R, McKee SA, Zhan J, Sun N. Digging deeper into cluster system logs for failure prediction and root cause diagnosis. In 2014 IEEE International Conference on Cluster Computing (CLUSTER), pages 103–112, Madrid, Spain, September 2014. IEEE.
He S, Lin Q, Lou J-G, Zhang H, Lyu MR, Zhang D. Identifying impactful service system problems via log analysis. In Proceedings of the 2018 26th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering - ESEC/FSE 2018, pages 60–70, Lake Buena Vista, FL, USA, 2018. ACM Press.
Lin Q, Zhang H, Lou J-G, Zhang Y, Chen X. Log clustering based problem identification for online service systems. In Proceedings of the 38th International Conference on Software Engineering Companion - ICSE ’16, pages 102–111, Austin, Texas, 2016. ACM Press.
Cito J, Leitner P, Gall HC, Dadashi A, Keller A, Roth A. Runtime metric meets developer: Building better cloud applications using feedback. In 2015 ACM International Symposium on New Ideas, New Paradigms, and Reflections on Programming and Software (Onward!) - Onward! 2015, pages 14–27, Pittsburgh, PA, USA, 2015. ACM Press.
Islam MS, Pourmajidi W, Zhang L, Steinbacher J, Erwin T, Miranskyy A. Anomaly detection in a large-scale cloud platform. In 2021 IEEE/ACM 43rd International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP), pages 150–159, 2021.
Gardin F, Gautier R, Goix N, Ndiaye B, Schertzer J-M. Machine learning with logical rules in Python. https://github.com/scikit-learn-contrib/skope-rules, 2020.
Friedman JH, Popescu BE. Predictive learning via rule ensembles. Ann Appl Stat. 2008;2(3):916–54.
Zhao Y, Nasrullah Z, Li Z. Pyod: A Python toolbox for scalable outlier detection. J Mach Learn Res. 2019;20(96):1–7.
Li Z, Zhao Y, Botta N, Ionescu C, Hu X. Copod: Copula-based outlier detection. In 2020 IEEE International Conference on Data Mining (ICDM), pages 1118–1123. IEEE, 09 2020.