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International Conference of the Italian Association for Artificial Intelligence

AIxIA 2021: AIxIA 2021 – Advances in Artificial Intelligence pp 591–606Cite as

Continuous Defect Prediction in CI/CD Pipelines: A Machine Learning-Based Framework

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Part of the Lecture Notes in Computer Science book series (LNAI,volume 13196)

Abstract

Recent advances in information technology has led to an increasing number of applications to be developed and maintained daily by product teams. Ensuring that a software application works as expected and that it is absent of bugs requires a lot of time and resources. Thanks to the recent adoption of DevOps methodologies, it is often the case where code commits and application builds are centralized and standardized. Thanks to this new approach, it is now possible to retrieve log and build data to ease the development and management operations of product teams. However, even if such approaches include code control to detect unit or integration errors, they do not check for the presence of logical bugs that can raise after code builds. For such reasons in this work we propose a framework for continuous defect prediction based on machine learning algorithms trained on a publicly available dataset. The framework is composed of a machine learning model for detecting the presence of logical bugs in code on the basis of the available data generated by DevOps tools and a dashboard to monitor the software projects status. We also describe the serverless architecture we designed for hosting the aforementioned framework.

Keywords

  • Continuous defect prediction
  • Machine learning
  • DevOps
  • Continuous integration

Activities were partially funded by Italian “Ministero dello Sviluppo Economico”, Fondo per la Crescita Sostenibile, Bando “Agenda Digitale”, D.M. Oct. 15th, 2014 - Project n. F/020012/02/X27 - “Smart District 4.0”.

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Authors and Affiliations

  1. Noovle S.p.A, Milan, Italy

    Lazzarinetti Giorgio, Massarenti Nicola, Sgrò Fabio & Salafia Andrea

Authors
  1. Lazzarinetti Giorgio
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  2. Massarenti Nicola
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  3. Sgrò Fabio
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  4. Salafia Andrea
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Corresponding author

Correspondence to Lazzarinetti Giorgio .

Editor information

Editors and Affiliations

  1. Department of Informatics, Systems and Communication, University of Milano-Bicocca, Milan, Italy

    Stefania Bandini

  2. Department of Informatics, Systems and Communication, University of Milano-Bicocca, Milan, Italy

    Francesca Gasparini

  3. Department of Informatics, Bioengineering, Robotics and Systems Engineering, University of Genoa, Genova, Italy

    Viviana Mascardi

  4. Department of Informatics, Systems and Communication, University of Milano-Bicocca, Milan, Italy

    Matteo Palmonari

  5. Department of Informatics, Systems and Communication, University of Milano-Bicocca, Milan, Italy

    Giuseppe Vizzari

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Giorgio, L., Nicola, M., Fabio, S., Andrea, S. (2022). Continuous Defect Prediction in CI/CD Pipelines: A Machine Learning-Based Framework. In: Bandini, S., Gasparini, F., Mascardi, V., Palmonari, M., Vizzari, G. (eds) AIxIA 2021 – Advances in Artificial Intelligence. AIxIA 2021. Lecture Notes in Computer Science(), vol 13196. Springer, Cham. https://doi.org/10.1007/978-3-031-08421-8_41

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  • DOI: https://doi.org/10.1007/978-3-031-08421-8_41

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-08420-1

  • Online ISBN: 978-3-031-08421-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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