Context:

Leveraging machine learning techniques to address feature location on models has been gaining attention. Machine learning techniques empower software product companies to take advantage of the knowledge and the experience to improve the performance of the feature location process. Most of the machine learning-based works for feature location on models report the machine learning techniques and the tuning parameters in detail. However, these works focus on the size and the distribution of the data sets, neglecting the properties of their contents.

Objective:

In this paper, we analyze the influence of three model fragment properties (density, multiplicity, and dispersion) on a machine learning-based approach for feature location.

Method:

The analysis of these properties is based on an industrial case provided by CAF, a worldwide provider of railway solutions. The test cases were evaluated through a machine learning technique that uses different subsets of a knowledge base to learn how to locate unknown features.

Results:

Results show that the density and dispersion properties have a direct impact on the results. In our case study, the model fragments with extra-small density values achieve results with up to 43% more precision, 41% more recall, 42% more F-measure, and 0.53 more Matthews Correlation Coefficient (MCC) than the model fragments with other density values. On the other hand, the model fragments with extra-small and small dispersion values achieve results with up to 53% more precision, 52% more recall, 52% more F-measure, and 0.57 more MCC than the model fragments with other dispersion values.

Conclusions:

The analysis of the results shows that both density and dispersion properties significantly influence the results. These results can serve not only to improve the reports by means of the model fragment properties, but also to be able to compare machine learning-based feature location approaches fairly improving the feature location results.

中文翻译：

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关于模型片段属性对基于机器学习的特征定位方法的影响

内容：

利用机器学习技术解决模型上特征的位置已引起关注。机器学习技术使软件产品公司能够利用知识和经验来改进要素定位过程的性能。在模型上进行特征定位的大多数基于机器学习的作品都详细报告了机器学习技术和调整参数。但是，这些工作集中在数据集的大小和分布上，而忽略了其内容的属性。

目的：

在本文中，我们分析了三个模型片段属性（密度，多重性和离散度）对基于机器学习的特征定位方法的影响。

方法：

对这些特性的分析是基于全球铁路解决方案提供商CAF提供的工业案例。通过机器学习技术对测试用例进行了评估，该技术使用知识库的不同子集来学习如何定位未知特征。

结果：

结果表明，密度和分散性能直接影响结果。在我们的案例研究中，与具有以下特征的模型片段相比，具有较小密度值的模型片段可实现以下结果：精度高43％，召回率提高41％，F测度提高42％，马修斯相关系数（MCC）高0.53。其他密度值。另一方面，与具有其他色散值的模型片段相比，具有极小的色散值和较小的色散值的模型片段可实现的结果具有高达53％的精度，52％的查全率，52％的F量度和0.57的MCC 。

结论：

对结果的分析表明，密度和分散性均会显着影响结果。这些结果不仅可以通过模型片段属性来改善报告，而且还可以比较基于机器学习的特征定位方法，从而公平地改善特征定位结果。