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Predicting change in newly created files in a software product line project
Software: Practice and Experience ( IF 3.5 ) Pub Date : 2020-10-13 , DOI: 10.1002/spe.2911
Yasser Ali Alshehri 1
Affiliation  

At the beginning of the testing phase and before the deployment phase of a project's development cycle, we need to predict files with a high chance of change. Software products are always prone to change due to several reasons, including fixing errors or improvements. In this work, we used the Eclipse (releases from 2.0 to 3.5) to investigate how prediction models can perform when learning from a release and predicting in the subsequent one, which contains new files that models have not seen. We compared the performance of these models with models that are trained and tested on the same release. We found no differences between predicting the same release or subsequent release on two pre Europa releases. Predicting change in newly created files helps improve maintenance planning for software project managers and reduce cost. It will also help to enhance the quality of software by improving the practices of developers. This study used the Adaptive Boost classifier with the decision tree J48 algorithm and combined it with the re-sampling method. We find this to be better than using a meta classifier alone or combine the re-sampling with the standard classification. We compared our results with related works and found that our results are outperforming.

中文翻译:

预测软件产品线项目中新创建文件的变化

在项目开发周期的测试阶段开始和部署阶段之前,我们需要预测具有高变化可能性的文件。由于多种原因,软件产品总是容易发生变化,包括修复错误或改进。在这项工作中,我们使用 Eclipse(从 2.0 到 3.5 的版本)来研究预测模型在从一个版本中学习并在后续版本中进行预测时如何执行,其中包含模型未见过的新文件。我们将这些模型的性能与在同一版本上训练和测试的模型进行了比较。我们发现在两个前 Europa 版本中预测相同版本或后续版本之间没有差异。预测新创建文件的变化有助于改进软件项目经理的维护计划并降低成本。它还将通过改进开发人员的实践来帮助提高软件的质量。本研究将自适应Boost分类器与决策树J48算法结合使用,并与重采样方法相结合。我们发现这比单独使用元分类器或将重采样与标准分类相结合要好。我们将我们的结果与相关工作进行了比较,发现我们的结果表现出色。
更新日期:2020-10-13
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