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首页> 外文期刊>International journal of open source software & processes >Ripple Effect Identification in Software Applications
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Ripple Effect Identification in Software Applications

机译：软件应用中的纹波效应识别

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摘要

Changes are made frequently in software to incorporate new requirements. The changes made to one class are not limited to that particular class, but they also affect other entities. Early identification of these change prone entities is very essential for minimizing future faults in the software applications. Thus, it is very important to develop quality models for identifying the ripple effect of changed classes to effectively utilize the limited resources during the software development lifecycle. Association rule mining is a popular approach suggested in literature, but a major limitation of this approach is its inability to generate recommendations in case of new addition of classes. This article suggests the development of prediction model using learning techniques to overcome this limitation. The authors evaluate the performance of thirteen statistical, ML, and search-based techniques using eight open source software applications in this work. The findings of this study are promising and support the application of SBT and ML techniques for ripple effect identification.
机译：在软件中经常进行更改以合并新的要求。对一个类别所做的更改不仅限于该特定类别，而且还影响其他实体。尽早识别这些易于更改的实体对于最大程度地减少软件应用程序中的未来故障至关重要。因此，开发质量模型以识别更改的类的连锁反应以在软件开发生命周期内有效利用有限的资源非常重要。关联规则挖掘是文献中建议的一种流行方法，但是这种方法的主要局限性是在添加新类时，它无法生成建议。本文建议使用学习技术克服这种局限性而开发预测模型。作者在这项工作中使用8个开源软件应用程序评估了13种统计，机器学习和基于搜索的技术的性能。这项研究的结果是有希望的，并支持SBT和ML技术在波纹效应识别中的应用。

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