通过适当的树匹配提高结构化合并的精度

Fengmin Zhu, Fei He, Q. Yu
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引用次数: 9

摘要

近年来,与传统的基于线的非结构化合并相比,结构化合并在提高合并精度方面显示出了优势。典型的结构化归并算法是在抽象语法树上进行匹配和合并。现有的树匹配技术的目标是通过最大化匹配节点的数量来找出最优匹配。然而,从现实世界的代码库来看,我们发现许多报告的冲突都是不必要的。我们提出了一个新的目标函数来定义合适的树匹配,它可以大大降低总体冲突率。我们对从18个开源项目中提取的3687个合并场景进行了实验。结果表明,该方法显著提高了合并精度。
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Enhancing Precision of Structured Merge by Proper Tree Matching
Recently, structured merge has shown its advantage in improving the merge precision over conventional line-based, unstructured merge. A typical structured merge algorithm consists of matching and amalgamation on abstract syntax trees. Existing tree matching techniques aim to figure out optimal matches by maximizing the number of matched nodes. From real-world codebases, however, we find that many of the reported conflicts are unnecessary. We propose a new objective function for defining a proper tree matching, with which the overall conflicting rate can be greatly reduced. We conducted experiments on 3,687 merge scenarios extracted from 18 open-source projects. Results show significant merge precision enhancement of our approach.
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