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A metrics-based approach for selecting among various refactoring candidates

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Abstract

Refactoring is the most prominent way of repaying Technical Debt and improving software maintainability. Despite the acknowledgement of refactorings as a state-of-practice technique (both by industry and academia), refactoring-based quality optimizations are debatable due to three important concerns: (a) the impact of a refactoring on quality is not always positive; (b) the list of available refactoring candidates is usually vast, restricting developers from applying all suggestions; and (c) there is no empirical evidence on which parameters are related to positive refactoring impact on quality. To alleviate these concerns, we reuse a benchmark (constructed in a previous study) of real-world refactorings having either a positive or negative impact on quality; and we explore the parameters (structural characteristics of classes) affecting the impact of the refactoring. Based on the findings, we propose a metrics-based approach for guiding practitioners on how to prioritize refactoring candidates. The results of the study suggest that classes with high coupling and large size should be given priority, since they tend to have a positive impact on technical debt.

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Data Availability

The dataset generated and analyzed during the current study is available online.

Notes

  1. https://github.com/nikosnikolaidis/github-pr-labels-filechanges

  2. https://github.com/dimizisis/metrics_calculator

  3. https://users.uom.gr/~a.ampatzoglou/aux_material/refactoring_preds.xlsx

  4. https://github.com/apache/pinot/commit/7d09489c5b939666c0561b6301c9287ef34ea239

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Authors and Affiliations

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

    Nikolaos Nikolaidis, Apostolos Ampatzoglou & Alexander Chatzigeorgiou

  2. Department of Chemistry, International Hellenic University, Kavala, Greece

    Nikolaos Mittas

  3. Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence, University of Groningen, Groningen, The Netherlands

    Daniel Feitosa

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  1. Nikolaos Nikolaidis
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Correspondence to Apostolos Ampatzoglou.

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Communicated by: Bibi Stamatia, Maxime Cordy, Bowen Xu, Xiaofei Xie

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Nikolaidis, N., Mittas, N., Ampatzoglou, A. et al. A metrics-based approach for selecting among various refactoring candidates. Empir Software Eng 29, 25 (2024). https://doi.org/10.1007/s10664-023-10412-w

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