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ACO based comprehensive model for software fault prediction

机译：基于ACO的软件故障预测综合模型

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摘要

The comprehensive models can be used for software quality modelling which involves prediction of low-quality modules using interpretable rules. Such comprehensive model can guide the design and testing team to focus on the poor quality modules, thereby, limited resources allocated for software quality inspection can be targeted only towards modules that are likely to be defective. Ant Colony Optimization (ACO) based learner is one potential way to obtain rules that can classify the software modules faulty and not faulty. This paper investigates ACO based mining approach with ROC based rule quality updation to constructs a rule-based software fault prediction model with useful metrics. We have also investigated the effect of feature selection on ACO based and other benchmark algorithms. We tested the proposed method on several publicly available software fault data sets. We compared the performance of ACO based learning with the results of three benchmark classifiers on the basis of area under the receiver operating characteristic curve. The evaluation of performance measure proves that the ACO based learner outperforms other benchmark techniques.
机译：综合模型可用于软件质量建模，涉及使用可解释规则预测低质量模块。这种综合模型可以指导设计和测试团队专注于劣质模块，从而，为软件质量检测分配的有限资源仅适用于可能有缺陷的模块。基于蚁群优化（ACO）的学习者是获得可以对软件模块故障进行分类而没有故障的规则的一种潜在方法。本文调查了基于ACO的挖掘方法与基于ROC的RULE质量更新，构建了一种具有有用度量的基于规则的软件故障预测模型。我们还调查了特征选择对ACO基础和其他基准算法的影响。我们在几种公开可用的软件故障数据集上测试了所提出的方法。我们将基于ACO的学习的性能与三个基准分类器的结果进行了比较，基于接收器操作特性曲线的区域。绩效措施的评估证明了基于ACO的学习者优于其他基准技术。

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