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An empirical study based on semi-supervised hybrid self-organizing map for software fault prediction

机译：基于半监督混合自组织图的软件故障预测实证研究

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摘要

Software testing is a crucial task during software development process with the potential to save time and budget by recognizing defects as early as possible and delivering a more defect-free product. To improve the testing process, fault prediction approaches identify parts of the system that are more defect prone. However, when the defect data or quality-based class labels are not identified or the company does not have similar or earlier versions of the software project, researchers cannot use supervised classification methods for defect detection. In order to detect defect proneness of modules in software projects with high accuracy and improve detection model generalization ability, we propose an automated software fault detection model using semi-supervised hybrid self-organizing map (HySOM). HySOM is a semi-supervised model based on self-organizing map and artificial neural network. The advantage of HySOM is the ability to predict the label of the modules in a semi-supervised manner using software measurement threshold values in the absence of quality data. In semi-supervised HySOM, the role of expert for identifying fault prone modules becomes less critical and more supportive. We have bench-marked the proposed model with eight industrial data sets from NASA and Turkish white-goods embedded controller software. The results show improvement in false negative rate and overall error rate in 80% and 60% of the cases respectively for NASA data sets. Moreover, we investigate the performance of the proposed model with other recent proposed methods. According to the results, our semi-supervised model can be used as an automated tool to guide testing effort by prioritizing the module's defects improving the quality of software development and software testing in less time and budget.
机译：软件测试是软件开发过程中的一项关键任务，它有可能通过尽早发现缺陷并交付更无缺陷的产品来节省时间和预算。为了改进测试过程，故障预测方法可以识别系统中更容易出现缺陷的部分。但是，如果未识别出缺陷数据或基于质量的类别标签，或者公司没有类似或较早版本的软件项目，则研究人员无法使用监督分类方法进行缺陷检测。为了高精度地检测软件项目中模块的缺陷倾向并提高检测模型的泛化能力，我们提出了一种使用半监督混合自组织图（HySOM）的自动化软件故障检测模型。 HySOM是基于自组织图和人工神经网络的半监督模型。 HySOM的优点是能够在没有质量数据的情况下使用软件测量阈值以半监督的方式预测模块的标签。在半监督的HySOM中，专家在识别易于发生故障的模块方面的作用变得不再那么关键，而更具支持性。我们使用来自NASA和土耳其白色家电嵌入式控制器软件的八个工业数据集对建议的模型进行了基准测试。结果表明，对于NASA数据集，假阴性率和总错误率分别提高了80％和60％。此外，我们使用其他最近提出的方法来研究提出的模型的性能。根据结果​​，我们的半监督模型可以作为自动化工具，通过优先考虑模块的缺陷来指导测试工作，从而以更少的时间和预算提高软件开发和软件测试的质量。

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