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• W3006458758 abstract "Root canal refactoring is a software development activity that is intended to improve dependability-related attributes such as modifiability and reusability. Despite being an activity that contributes to these attributes, deciding when applying root canal refactoring is far from trivial. In fact, finding which elements should be refactored is not a cut-and-dried task. One of the main reasons is the lack of consensus on which characteristics indicate the presence of structural degradation. Thus, we evaluated whether the density and diversity of multiple automatically detected symptoms can be used as consistent indicators of the need for root canal refactoring. To achieve our goal, we conducted a multi-case exploratory study involving 6 open source systems and 2 systems from our industry partners. For each system, we identified the classes that were changed through one or more root canal refactorings. After that, we compared refactored and non-refactored classes with respect to the density and diversity of degradation symptoms. We also investigated if the most recurrent combinations of symptoms in refactored classes can be used as strong indicators of structural degradation. Our results show that refactored classes usually present higher density and diversity of symptoms than non-refactored classes. However, root canal refactorings that are performed by developers in practice may not be enough for reducing degradation, since the vast majority had little to no impact on the density and diversity of symptoms. Finally, we observed that symptom combinations in refactored classes are similar to the combinations in non-refactored classes. Based on our findings, we elicited an initial set of requirements for automatically recommending root canal refactorings." @default.
• W3006458758 created "2020-02-24" @default.
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• W3006458758 date "2019-10-01" @default.
• W3006458758 modified "2023-10-05" @default.
• W3006458758 title "On the Density and Diversity of Degradation Symptoms in Refactored Classes: A Multi-case Study" @default.
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• W3006458758 doi "https://doi.org/10.1109/issre.2019.00042" @default.
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