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Home > Archive>Volume 28, Issue 6, 2017 >1418-1434. DOI:10.13328/j.cnki.jos.005225
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Auxiliary Method for Code Commit Comprehension Based on Core-Class Identification
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NSFC-Guangdong Joint Fund (U1201252); National Key Research and Development Program of China (2016YFB1000101); National Natural Science Foundation of China Science and Technology (61672545, 61672045); Science and Technology Planning Project of Guangdong Province (2015B040403005)

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    Abstract:

    Code commit is one of the most important software evolution data, and it is widely used in the software review and code comprehension. A commit involving multiple modified classes and code makes the review of code changes difficult. By analyzing a large amount of commit data, this study discovers that identifying the core modified classes in a commit can speed up commit review for developers. Inspired by the effectiveness of machine learning techniques in classification, the paper models the core class identification as a binary classification problem (i.e., core and non-core) and proposes discriminative features from a large number of commits to characterize the core modified classes. The experiments results show that the proposed approach achieves 87% accuracy, and using core class in commit review provides significant improvement than the ones without core class.

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黄袁,刘志勇,陈湘萍,熊英飞,罗笑南.基于关键类判定的代码提交理解辅助方法.软件学报,2017,28(6):1418-1434

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History
  • Received:July 28,2016
  • Revised:October 11,2016
  • Online: February 21,2017
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