首页> 外文期刊>Software and systems modeling >An approach for bug localization in models using two levels: model and metamodel
【24h】

An approach for bug localization in models using two levels: model and metamodel

机译:使用两个级别在模型中进行错误定位的方法:模型和元模型

获取原文
获取原文并翻译 | 示例

摘要

Bug localization is a common task in software engineering, especially when maintaining and evolving software products. This paper introduces a bug localization approach that, in contrast to existing source code approaches, takes advantage of domain information found in the model and the metamodel. Throughout this paper, we present an approach for bug localization in models (BLiM2) that applies the source code ideas for bug localization (textual similarity to the bug description and the Defect Localization Principle) and takes advantage of the domain information from the model and the metamodel. We evaluated our approach in BSH, a real-world industrial case study in the induction hob domain measuring the results in terms of recall, precision, the combination of both the F-measure and the Matthews correlation coefficient. Our study shows that our BLiM2 approach, which combines information from the model and the metamodel for the textual similarity and differentiates between the timespan from the model and metamodel, provides the best results in this work. We also performed a statistical analysis to provide evidence of the significance of the results. The values obtained show that there exist significant differences in the performance of the best BLiM2 approach with the approach used by our industrial partner. Finally, the effect size statistics reveals that the best BLiM2 approach obtains better results in the 78% of the times in the worst case.
机译:错误本地化是软件工程中的常见任务,尤其是在维护和发展软件产品时。本文介绍了一种错误定位方法,与现有的源代码方法相比,该方法利用了模型和元模型中的域信息。在整个本文中,我们提供了一种模型中的错误本地化方法(BLiM2),该方法将源代码思想应用于错误本地化(与错误描述和缺陷本地化原理的文本相似性),并利用了模型和元模型。我们评估了BSH的方法,BSH是感应滚刀领域的实际案例研究,在召回率,精度,F量度和Matthews相关系数的组合方面对结果进行测量。我们的研究表明,我们的BLiM2方法结合了来自模型和元模型的信息以实现文本相似性,并区分了来自模型和元模型的时间跨度,在这项工作中提供了最佳的结果。我们还进行了统计分析以提供结果意义的证据。所获得的值表明,最佳BLiM2方法的性能与我们的工业合作伙伴所使用的方法之间存在显着差异。最后,效应量统计数据表明,在最坏的情况下,最佳的BLiM2方法在78%的时间内可获得更好的结果。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号