通过使用静态度量来预测代码变更

Andreas Mauczka, T. Grechenig, Mario Bernhart
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引用次数: 8

摘要

软件的维护是有风险的,潜在的昂贵的——而且是不可避免的。本研究的主要目标是检查代码变更(称为维护工作)与源代码级软件度量的关系。这种方法不同于针对失败数据评估软件度量的典型方法,并提供了对软件度量验证的不同角度。本研究的目标是通过详尽的数据挖掘来显示软件度量和代码变更之间存在的关系。一旦建立了这种联系,就确定了一组软件度量标准,这将在进一步的研究中使用,以预测在早期开发阶段由软件度量标准确定的有问题模块中的代码更改。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Predicting Code Change by Using Static Metrics
Maintenance of software is risky, potentially expensive – and inevitable. The main objective of this study is to examine the relationship of code change, referred to as maintenance effort, with source-level software metrics. This approach varies from the typical approach of evaluating software metrics against failure data and provides a different angle on the validation of software metrics. The goal of this study is to show through exhaustive data mining that a relation between software metrics and code change exists. Once this connection is established, a set of software metrics is identified, which will be used in further studies to predict code change in problematic modules identified by the software metrics at an early development stage.
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