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Empirical Study about Class Change Proneness Prediction using Software Metrics and Code Smells

机译：使用软件度量和代码闻到阶级改变阶级的实证研究

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摘要

During the lifecycle of software, maintenance has been considered one of the most complex and costly phases in terms of resources and costs. In addition, software evolves in response to the needs and demands of the ever-changing world and thus becomes increasingly complex. In this scenario, an approach that has been widely used to rationalize resources and costs during the evolution of object-oriented software is to predict change-prone classes. A change-prone class may indicate a part of poor quality of software that needs to be refactored. Recently, some strategies for predicting change-prone classes, which are based on the use of software metrics and code smells, have been proposed. In this paper, we present an empirical study on the performance of 8 machine learning techniques used to predict classes prone to change. Three different training scenarios were investigated: object-oriented metrics, code smells, and object-oriented metrics and code smells combined. To perform the experiments, we built a data set containing eight object-oriented metrics and 32 types of code smells, which were extracted from the source code of a web application that was developed between 2013 and 2018 over eight releases. The machine learning algorithms that presented the best results were: RF, LGBM, and LR. The training scenario that presented the best results was the combination of code smells and object-oriented metrics.
机译：在软件的生命周期中，维护被认为是资源和成本中最复杂和最昂贵的阶段之一。此外，软件在响应不断变化的世界的需求和需求时发展，因此变得越来越复杂。在这种情况下，一种已被广泛用于在面向对象软件演变期间合理化资源和成本的方法是预测变化易于的课程。变更易于阶级可能表明需要重构的软件质量差的一部分。最近，已经提出了一些预测变化易于课程的一些策略，这是基于软件度量和代码闻的使用。在本文中，我们对用于预测容易改变的课程的8种机器学习技术的性能进行了实证研究。调查了三种不同的训练场景：面向对象的指标，代码闻和面向对象的指标和代码闻。为了执行实验，我们建立了一个包含八个面向对象度量的数据集和32种类型的代码气味，这些商品是从2013年和2018年之间开发的Web应用程序的源代码中提取，这些标号在八个版本中开发。呈现最佳结果的机器学习算法是：RF，LGBM和LR。展示最佳结果的培训方案是代码嗅觉和面向对象的指标的组合。

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