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Applying Machine Learning in Technical Debt Management: Future Opportunities and Challenges

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Quality of Information and Communications Technology (QUATIC 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1266))

Abstract

Technical Debt Management (TDM) is a fast-growing field that in the last years has attracted the attention of both academia and industry. TDM is a complex process, in the sense that it relies on multiple and heterogeneous data sources (e.g., source code, feature requests, bugs, developers’ activity, etc.), which cannot be straightforwardly synthesized; leading the community to using mostly qualitative empirical methods. However, empirical studies that involve expert judgement are inherently biased, compared to automated or semi-automated approaches. To overcome this limitation, the broader (not TDM) software engineering community has started to employ machine learning (ML) technologies. Our goal is to investigate the opportunity of applying ML technologies for TDM, through a Systematic Literature Review (SLR) on the application of ML to software engineering problems (since ML applications on TDM are limited). Thus, we have performed a broader scope study, i.e., on machine learning for software engineering, and then synthesize the results so as to achieve our high-level goal (i.e., possible application of ML in TDM). Therefore, we have conducted a literature review, by browsing the research corpus published in five high-quality SE journals, with the goal of cataloging: (a) the software engineering practices in which ML is used; (b) the machine learning technologies that are used for solving them; and (c) the intersection of the two: develo** a problem-solution map**. The results are useful to both academics and industry, since the former can identify possible gaps, and interesting future research directions, whereas the latter can obtain benefits by adopting ML technologies.

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Acknowledgements

Work reported in this paper has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 871177 (project: SmartCLIDE).

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Authors and Affiliations

  1. Department of Applied Informatics, University of Macedonia, Thessaloniki, Greece

    Angeliki-Agathi Tsintzira, Elvira-Maria Arvanitou, Apostolos Ampatzoglou & Alexander Chatzigeorgiou

Authors
  1. Angeliki-Agathi Tsintzira
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  2. Elvira-Maria Arvanitou
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  3. Apostolos Ampatzoglou
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  4. Alexander Chatzigeorgiou
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Corresponding author

Correspondence to Elvira-Maria Arvanitou .

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Editors and Affiliations

  1. Brunel University, London, UK

    Martin Shepperd

  2. Lisbon University Institute, Lisbon, Portugal

    Fernando Brito e Abreu

  3. University of Lisbon, Lisbon, Portugal

    Alberto Rodrigues da Silva

  4. University of Castilla-La Mancha, Talavera de la Reina, Spain

    Ricardo Pérez-Castillo

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Tsintzira, AA., Arvanitou, EM., Ampatzoglou, A., Chatzigeorgiou, A. (2020). Applying Machine Learning in Technical Debt Management: Future Opportunities and Challenges. In: Shepperd, M., Brito e Abreu, F., Rodrigues da Silva, A., Pérez-Castillo, R. (eds) Quality of Information and Communications Technology. QUATIC 2020. Communications in Computer and Information Science, vol 1266. Springer, Cham. https://doi.org/10.1007/978-3-030-58793-2_5

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  • DOI: https://doi.org/10.1007/978-3-030-58793-2_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-58792-5

  • Online ISBN: 978-3-030-58793-2

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