AURA: a hybrid approach to identify framework evolution
AURA: a hybrid approach to identify framework evolution
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DOI：
10.1145/1806799.1806848
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发表时间：
2010-05
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通讯作者：
Wei Wu;Yann-Gaël Guéhéneuc;G. Antoniol;Miryung Kim
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文献类型：
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作者：
Wei Wu;Yann-Gaël Guéhéneuc;G. Antoniol;Miryung Kim
随着它们的发展，软件框架和库是当今软件系统的必要条件。通过一个或多个实验评估的阈值，他们对逐个修复的规则和多种方法进行了更高的召回Aura是一种新型混合方法，结合了呼叫依赖性和文本相似性分析以克服这些局限性。等等。比较表明，平均而言，Aura的召回率高为53.07％，而其精度相似，例如低于0.10％。
Software frameworks and libraries are indispensable to today's software systems. As they evolve, it is often time-consuming for developers to keep their code up-to-date, so approaches have been proposed to facilitate this. Usually, these approaches cannot automatically identify change rules for one-replaced-by-many and many-replaced-by-one methods, and they trade off recall for higher precision using one or more experimentally-evaluated thresholds. We introduce AURA, a novel hybrid approach that combines call dependency and text similarity analyses to overcome these limitations. We implement it in a Java system and compare it on five frameworks with three previous approaches by Dagenais and Robillard, M. Kim et al., and Schäfer et al. The comparison shows that, on average, the recall of AURA is 53.07% higher while its precision is similar, e.g., 0.10% lower.