Anomaly Detection in DevOps Toolchain

Antonio Capizzi; Salvatore Distefano; Luiz J.P. Araújo; Manuel Mazzara; Muhammad Ahmad; Evgeny Bobrov

doi:10.1007/978-3-030-39306-9_3

Anomaly Detection in DevOps Toolchain

Antonio Capizzi, Salvatore Distefano, Luiz J.P. Araújo, Manuel Mazzara^*, Muhammad Ahmad, Evgeny Bobrov

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

7 Scopus citations

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.

Original language	English
Title of host publication	Software Engineering Aspects of Continuous Development and New Paradigms of Software Production and Deployment - 2nd International Workshop, DEVOPS 2019, Revised Selected Papers
Editors	Jean-Michel Bruel, Manuel Mazzara, Bertrand Meyer
Publisher	Springer
Pages	37-51
Number of pages	15
ISBN (Print)	9783030393052
DOIs	https://doi.org/10.1007/978-3-030-39306-9_3
State	Published - 2020
Externally published	Yes
Event	2nd International Workshop on Software Engineering Aspects of Continuous Development and New Paradigms of Software Production and Deployment, DEVOPS 2019 - Villebrumier, France Duration: 6 May 2019 → 8 May 2019

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12055 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	2nd International Workshop on Software Engineering Aspects of Continuous Development and New Paradigms of Software Production and Deployment, DEVOPS 2019
Country/Territory	France
City	Villebrumier
Period	6/05/19 → 8/05/19

Bibliographical note

Publisher Copyright:
© 2020, Springer Nature Switzerland AG.

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-030-39306-9_3

Cite this

Capizzi, A., Distefano, S., Araújo, L. J. P., Mazzara, M., Ahmad, M., & Bobrov, E. (2020). Anomaly Detection in DevOps Toolchain. In J.-M. Bruel, M. Mazzara, & B. Meyer (Eds.), Software Engineering Aspects of Continuous Development and New Paradigms of Software Production and Deployment - 2nd International Workshop, DEVOPS 2019, Revised Selected Papers (pp. 37-51). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12055 LNCS). Springer. https://doi.org/10.1007/978-3-030-39306-9_3

Capizzi, Antonio ; Distefano, Salvatore ; Araújo, Luiz J.P. et al. / Anomaly Detection in DevOps Toolchain. Software Engineering Aspects of Continuous Development and New Paradigms of Software Production and Deployment - 2nd International Workshop, DEVOPS 2019, Revised Selected Papers. editor / Jean-Michel Bruel ; Manuel Mazzara ; Bertrand Meyer. Springer, 2020. pp. 37-51 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Anomaly Detection in DevOps Toolchain",

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.",

author = "Antonio Capizzi and Salvatore Distefano and Ara{\'u}jo, \{Luiz J.P.\} and Manuel Mazzara and Muhammad Ahmad and Evgeny Bobrov",

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Capizzi, A, Distefano, S, Araújo, LJP, Mazzara, M, Ahmad, M & Bobrov, E 2020, Anomaly Detection in DevOps Toolchain. in J-M Bruel, M Mazzara & B Meyer (eds), Software Engineering Aspects of Continuous Development and New Paradigms of Software Production and Deployment - 2nd International Workshop, DEVOPS 2019, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12055 LNCS, Springer, pp. 37-51, 2nd International Workshop on Software Engineering Aspects of Continuous Development and New Paradigms of Software Production and Deployment, DEVOPS 2019, Villebrumier, France, 6/05/19. https://doi.org/10.1007/978-3-030-39306-9_3

Anomaly Detection in DevOps Toolchain. / Capizzi, Antonio; Distefano, Salvatore; Araújo, Luiz J.P. et al.
Software Engineering Aspects of Continuous Development and New Paradigms of Software Production and Deployment - 2nd International Workshop, DEVOPS 2019, Revised Selected Papers. ed. / Jean-Michel Bruel; Manuel Mazzara; Bertrand Meyer. Springer, 2020. p. 37-51 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12055 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Distefano, Salvatore

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AU - Ahmad, Muhammad

AU - Bobrov, Evgeny

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ER -

Capizzi A, Distefano S, Araújo LJP, Mazzara M, Ahmad M, Bobrov E. Anomaly Detection in DevOps Toolchain. In Bruel JM, Mazzara M, Meyer B, editors, Software Engineering Aspects of Continuous Development and New Paradigms of Software Production and Deployment - 2nd International Workshop, DEVOPS 2019, Revised Selected Papers. Springer. 2020. p. 37-51. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-39306-9_3

Anomaly Detection in DevOps Toolchain

Abstract

Publication series

Conference

Bibliographical note

ASJC Scopus subject areas

Access to Document

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Cite this