On the Effectiveness of Bug Predictors with Procedural Systems: A Quantitative Study
On the Effectiveness of Bug Predictors with Procedural Systems: A Quantitative Study
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DOI：
10.1007/978-3-662-54494-5_5
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发表时间：
2017-04
期刊：
影响因子：
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通讯作者：
C. W. Araújo;Ingrid Nunes;D. Nunes
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文献类型：
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作者：
C. W. Araújo;Ingrid Nunes;D. Nunes
许多错误预测器已经被提出，它们的主要目标是面向对象的系统。虽然面向对象是目前大多数软件应用程序的选择，但过程范式仍被用于许多（有时是关键的）应用程序，如操作系统和嵌入式系统。因此，它们也值得关注。我们提出了一项研究，我们调查了现有的错误预测方法与程序系统的有效性。这种方法使用静态代码度量作为输入。我们评估了它们在多大程度上适用于我们的环境，并使用标准指标比较了它们的有效性，并在需要时进行了调整。我们评估了五种方法，使用八个程序软件系统，包括开源和工业项目。我们得出的结论是，代码行是在我们的上下文中起关键作用的指标，使用大量指标的方法可能会在预测模型中引入噪声。此外，开放源代码系统取得了最好的结果。
Many bug predictors have been proposed, and their main target is object-oriented systems. Although object-orientation is currently the choice for most of the software applications, the procedural paradigm is still being used in many—sometimes crucial—applications, such as operating systems and embedded systems. Consequently, they also deserve attention. We present a study in which we investigated the effectiveness of existing bug prediction approaches with procedural systems. Such approaches use as input static code metrics. We evaluated to what extent they are applicable to our context, and compared their effectiveness using standard metrics, with adaptations when needed. We assessed five approaches, using eight procedural software systems, including open-source and industrial projects. We concluded that lines of code is the metric that plays the key role in our context, and approaches that use of a large set of metrics can introduce noise in the prediction model. In addition, the best results were obtained with open-source systems.