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A Semiautomatic Approach to Identify Architectural Technical Debt from Heterogeneous Artifacts

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Software Architecture (ECSA 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1269))

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Abstract

Architectural Technical Debt (ATD) is a metaphor used to describe decisions taken by software architects to accomplish short-term goals but possibly negatively affecting the long-term health of the system. However, ATD doesn’t receive enough attention for the architect teams because it is hard to identify, to measure, to prioritize, and its value is related to long-term maintenance and evolution of a system. In this research, we propose a model-driven approach that focuses on building a binary classification model for ATD identification based on the information gathered from artifacts produced during architecture design. This model will allow software architects to support the managing of conscious and unconscious ATD in their software projects. This proposal focuses on TD at the architecture-level only without considering source code. The effectiveness of this proposal will be evaluated using case studies and expert interviews.

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Notes

  1. 1.

    (Meta)model of Eclipse Modeling Framework (EMF).

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Acknowledgments

The author is working under the supervision of Prof. Darío Correal.

Author information

Authors and Affiliations

  1. Universidad de los Andes, Bogotá, Colombia

    Boris Pérez

  2. Univ. Francisco de Paula Santander, Cúcuta, Colombia

    Boris Pérez

Authors
  1. Boris Pérez
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Correspondence to Boris Pérez .

Editor information

Editors and Affiliations

  1. University of L’Aquila, L’Aquila, Italy

    Henry Muccini

  2. University of Groningen, Groningen, The Netherlands

    Paris Avgeriou

  3. Faculty of Informatics, Masaryk University, Brno, Czech Republic

    Barbora Buhnova

  4. Department of Computer Science, University of York, York, UK

    Javier Camara

  5. Department of Computer Science, Linnaeus University, Växjö, Sweden

    Mauro Caporuscio

  6. University of L’Aquila, L’Aquila, Italy

    Mirco Franzago

  7. Karlsruhe Institute of Technology, Karlsruhe, Germany

    Anne Koziolek

  8. University of Bergamo, Dalmine, Italy

    Patrizia Scandurra

  9. Gran Sasso Science Institute, L’Aquila, Italy

    Catia Trubiani

  10. Department of Computer Science, KU Leuven, Leuven, Belgium

    Danny Weyns

  11. Faculty of Computer Science, University of Vienna, Vienna, Austria

    Uwe Zdun

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Pérez, B. (2020). A Semiautomatic Approach to Identify Architectural Technical Debt from Heterogeneous Artifacts. In: Muccini, H., et al. Software Architecture. ECSA 2020. Communications in Computer and Information Science, vol 1269. Springer, Cham. https://doi.org/10.1007/978-3-030-59155-7_1

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  • DOI: https://doi.org/10.1007/978-3-030-59155-7_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-59154-0

  • Online ISBN: 978-3-030-59155-7

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