宏观共变的探索性研究

Fehmi Jaafar, Yann-Gaël Guéhéneuc, Sylvie Hamel, G. Antoniol
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引用次数: 34

摘要

文献描述了几种方法来识别一起更改的程序的工件,以揭示这些工件之间的(隐藏的)依赖关系。这些方法分析从版本控制系统中挖掘的历史数据,并报告共同更改的工件,这些工件暗示了更改的原因、结果和参与者。我们引入了宏观共变(MCC)和去相宏观共变(DMCC)的新概念,即在大时间间隔内共变的伪相,即总是在相同的时间位移中发生的宏观共变。我们描述了MCC和DMCC的典型情景,并使用汉明距离来检测MCC和DMCC的近似发生情况。我们提出了在大型程序中识别这些概念的方法——Macocha。我们在四个系统上应用Macocha,并将其与UML Diff(文件稳定性)和关联规则(共同更改文件)在精度和召回方面进行比较:Argo UML、Free BSD、SIP和XalanC。我们还使用外部信息来验证Macocha发现的(近似)MCC和DMCC。因此,我们回答了两个研究问题,展示了这些概念的存在和有用性,并解释了人工制品之间隐藏依赖的场景。
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An Exploratory Study of Macro Co-changes
The literature describes several approaches to identify the artefacts of programs that change together to reveal the (hidden) dependencies among these artefacts. These approaches analyse historical data, mined from version control systems, and report co-changing artefacts, which hint at the causes, consequences, and actors of the changes. We introduce the novel concepts of macro co-changes (MCC), i.e., of artefacts that co-change within a large time interval, and of dephase macro co-changes (DMCC), i.e., macro co-changes that always happen with the same shifts in time. We describe typical scenarios of MCC and DMCC and we use the Hamming distance to detect approximate occurrences of MCC and DMCC. We present our approach, Macocha, to identify these concepts in large programs. We apply Macocha and compare it in terms of precision and recall with UML Diff (file stability) and association rules (co-changing files) on four systems: Argo UML, Free BSD, SIP, and XalanC. We also use external information to validate the (approximate) MCC and DMCC found by Macocha. We thus answer two research questions showing the existence and usefulness of theses concepts and explaining scenarios of hidden dependencies among artefacts.
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