Abstract
The tools employed in the DevOps Toolchain generates a large quantity of data that is typically ignored or inspected only on particular occasions, at most. However, the analysis of such data could enable the extraction of useful information about the status and evolution of the project. For example, metrics like the “lines of code added since the last release” or “failures detected in the staging environment” are good indicators for predicting potential risks in the incoming release. In order to prevent problems appearing in later stages of production, an anomaly detection system can operate in the staging environment to compare the current incoming release with previous ones according to predefined metrics. The analysis is conducted before going into production to identify anomalies which should be addressed by human operators that address false-positive and negatives that can appear. In this paper, we describe a prototypical implementation of the aforementioned idea in the form of a “proof of concept”. The current study effectively demonstrates the feasibility of the approach for a set of implemented functionalities.
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References
Wasserman, A.I.: Modern software development methodologies and their environments. Comput. Phys. Commun. 38(2), 119–134 (1985)
Bass, L., Weber, I., Zhu, L.: DevOps: A Software Architect’s Perspective, 1st edn. Addison-Wesley Professional, Boston (2015)
Bruel, J.-M., Mazzara, M., Meyer, B. (eds.): DEVOPS 2018. LNCS, vol. 11350. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-06019-0
Hopcroft, J.E., Motwani, R., Ullman, J.D.: Introduction to Automata Theory, Languages, and Computation, 3rd edn. Pearson International Edition, Addison-Wesley, Boston (2007)
Kersten, M.: A cambrian explosion of devops tools. IEEE Softw. 35, 14–17 (2018)
Protasov, S., Khan, A.M., Sozykin, K., Ahmad, M.: Using deep features for video scene detection and annotation. SIViP 12, 991–999 (2018)
Kontogiannis, K., et al.: 2nd workshop on DevOps and software analytics for continuous engineering and improvement. In: Proceedings of the 28th Annual International Conference on Computer Science and Software Engineering, CASCON 2018, Riverton, NJ, USA, pp. 369–370. IBM Corp (2018)
Akinsanya, B., et al.: Machine learning and value generation in software development: a survey. In: Software Testing, Machine Learning and Complex Process Analysis (TMPA 2019), pp. 1–10. Springer International Publishing (2019, forthcoming)
Li, Z., Dang, Y.: AIOps: Challenges and Experiences in Azure. USENIX Association, Santa Clara (2019)
Yang, Y., Falessi, D., Menzies, T., Hihn, J.: Actionable analytics for software engineering. IEEE Softw. 35, 51–53 (2018)
Hoffman, J.: How AIOps Supports a DevOps World. https://thenewstack.io/how-aiops-supports-a-devops-world/
Snyder, B., Curtis, B.: Using analytics to guide improvement during an Agile-DevOps transformation. IEEE Softw. 35, 78–83 (2018)
Guo, C., Wang, H., Dai, H., Cheng, S., Wang, T.: Fraud risk monitoring system for e-banking transactions. In: 2018 IEEE 16th Intl Conf on Dependable, Autonomic and Secure Computing, 16th Intl Conf on Pervasive Intelligence and Computing, 4th Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress (DASC/PiCom/DataCom/CyberSciTech), pp. 100–105, August 2018
Chen, P., Yang, S., McCann, J.A.: Distributed real-time anomaly detection in networked industrial sensing systems. IEEE Trans. Industr. Electron. 62, 3832–3842 (2015)
Chandola, V., Banerjee, A., Kumar, V.: Anomaly Detection, pp. 1–15. Springer, Boston (2016). https://doi.org/10.1007/978-1-4899-7502-7
Sun, D., Fu, M., Zhu, L., Li, G., Lu, Q.: Non-intrusive anomaly detection with streaming performance metrics and logs for devops in public clouds: a case study in aws. IEEE Trans. Emerg. Top. Comput. 4, 278–289 (2016)
Hodge, V.J., Austin, J.: A survey of outlier detection methodologies. Artif. Intell. Rev. 22, 85–126 (2004)
Capizzi, A.: SpaceViewer - a ReactJS portal for NASA Open API consultation. https://github.com/antoniocapizzi95/SpaceViewer/
Facebook: ReactJS - A JavaScript library for building user interfaces. https://reactjs.org/
NASA: NASA Open API. https://api.nasa.gov/
Capizzi, A.: SpaceViewer - little back end. https://github.com/antoniocapizzi95/SpaceViewer_BE/
P. S. Foundation: Python - Programming Language. https://www.python.org/
T. P. Projects: Flask is a lightweight WSGI web application framework. https://palletsprojects.com/p/flask/
Kawaguchi, K.: Jenkins - an open source automation server which enables developers around the world to reliably build, test, and deploy their software. https://jenkins.io/
Preston-Werner, S.C.P.J.H.T., Wanstrath, C.: GitHub - The world’s leading software development platform. https://github.com/
CodeClimate: CodeClimate Quality. https://codeclimate.com/quality/
Docker, I.: Docker - Build, Share, and Run Any App, Anywhere. https://www.docker.com/
S. Technologies: Slack is where work flows. It’s where the people you need, the information you share, and the tools you use come together to get things done. https://slack.com/
Schlueter, K.M.I.Z., Turner, R.: Node Package Manager. https://www.npmjs.com/
Capizzi, A.: Anomaly Detection System used for SpaceViewer DevOps Toolchain. https://github.com/antoniocapizzi95/SpaceViewer_ADS/
Kawaguchi, K.: Jenkins Pipeline Documentation. https://jenkins.io/doc/book/pipeline/
Jacomb, T.: Slack Notification Plugin for Jenkins. https://plugins.jenkins.io/slack
scikit learn: Novelty detection with Local Outlier Factor (LOF). https://scikit-learn.org/stable/auto_examples/neighbors/plot_lof_novelty_detection.html/
Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)
Mazzara, M., Naumchev, A., Safina, L., Sillitti, A., Urysov, K.: Teaching DevOps in corporate environments. In: Bruel, J.-M., Mazzara, M., Meyer, B. (eds.) DEVOPS 2018. LNCS, vol. 11350, pp. 100–111. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-06019-0_8
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Capizzi, A., Distefano, S., Araújo, L.J.P., Mazzara, M., Ahmad, M., Bobrov, E. (2020). Anomaly Detection in DevOps Toolchain. In: Bruel, JM., Mazzara, M., Meyer, B. (eds) Software Engineering Aspects of Continuous Development and New Paradigms of Software Production and Deployment. DEVOPS 2019. Lecture Notes in Computer Science(), vol 12055. Springer, Cham. https://doi.org/10.1007/978-3-030-39306-9_3
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