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Empirical Software Engineering

Erschienen in:

01.02.2024

A metrics-based approach for selecting among various refactoring candidates

verfasst von: Nikolaos Nikolaidis, Nikolaos Mittas, Apostolos Ampatzoglou, Daniel Feitosa, Alexander Chatzigeorgiou

Erschienen in: Empirical Software Engineering | Ausgabe 1/2024

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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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Titel: A metrics-based approach for selecting among various refactoring candidates
verfasst von: Nikolaos Nikolaidis
Nikolaos Mittas
Apostolos Ampatzoglou
Daniel Feitosa
Alexander Chatzigeorgiou
Publikationsdatum: 01.02.2024
Verlag: Springer US
Erschienen in: Empirical Software Engineering / Ausgabe 1/2024
Print ISSN: 1382-3256
Elektronische ISSN: 1573-7616
DOI: https://doi.org/10.1007/s10664-023-10412-w

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