Context:

Code Clones have been accepted as a common phenomenon in software, thanks to the increasing demand for rapid production of software. The existence of code clones is recognized by developers in the form of clone group, which includes several pieces of clone fragments that are similar to one another. A change in one of these clone fragments may indicate necessary “consistent changes” are required for the rest of the clones within the same group, which can increase extra maintenance costs. A failure in making such consistent change when it is necessary is commonly known as a “clone consistency-defect”, which can adversely impact software maintainability.

Objective:

Predicting the need for “clone consistent changes” after successful clone-creating or clone-changing operations can help developers maintain clone changes effectively, avoid consistency-defects and reduce maintenance cost.

Method:

In this work, we use several sets of attributes in two scenarios of clone operations (clone-creating and clone-changing), and conduct an empirical study on five different machine-learning methods to assess each of their clone consistency predictability — whether any one of the clone operations will require or be free of clone consistency maintenance in future.

Results:

We perform our experiments on eight open-source projects. Our study shows that such predictions can be reasonably effective both for clone-creating and changing operating instances. We also investigate the use of five different machine-learning methods for predictions and show that our selected features are effective in predicting the needs of consistency-maintenance across all selected machine-learning methods.

Conclusion:

The empirical study conducted here demonstrates that the models developed by different machine-learning methods with the specified sets of attributes have the ability to perform clone-consistency prediction.

中文翻译：

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基于机器学习的克隆一致性预测的实证研究

语境：

由于对快速生产软件的需求不断增加，代码克隆已被接受为软件中的普遍现象。开发人员以克隆组的形式识别代码克隆的存在，克隆组包括几块彼此相似的克隆片段。这些克隆片段之一的更改可能表明同一组中其余克隆都需要进行必要的“一致更改”，这可能会增加额外的维护成本。在必要时进行此类一致更改的失败通常被称为“克隆一致性缺陷”，这可能会对软件的可维护性产生不利影响。

客观的：

成功创建克隆或更改克隆后，预测是否需要“克隆一致性更改”，可以帮助开发人员有效地维护克隆更改，避免一致性缺陷并降低维护成本。

方法：

在这项工作中，我们在两种克隆操作方案中（克隆创建和克隆更改）使用了几组属性，并对五种不同的机器学习方法进行了实证研究，以评估它们的每种克隆一致性可预测性-是否有任何一种克隆操作的将来将需要或没有克隆一致性维护。

结果：

我们在八个开源项目上进行了实验。我们的研究表明，这样的预测对于创建克隆和更改操作实例都可以相当有效。我们还研究了使用五种不同的机器学习方法进行预测，并表明我们选择的功能可以有效地预测所有选择的机器学习方法之间保持一致性的需求。

结论：

此处进行的经验研究表明，由具有指定属性集的不同机器学习方法开发的模型具有执行克隆一致性预测的能力。