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首页> 外文期刊>International Journal of Computer Science and Technology >A Collective Methodology of Software Metrics and Software Fault Exploration to Software Dependability Approximation
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A Collective Methodology of Software Metrics and Software Fault Exploration to Software Dependability Approximation

机译：一套软件度量和软件故障探索的方法论

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The accurate prediction of where faults are probably going to happen in code with coordinate test exertion, lessen costs and enhance the quality of software. Target of this paper is we research how the setting of models, the autonomous factors utilized and the demonstrating methods connected, impact the execution of fault prediction models. In this paper we have indicated compherensive assessment of various machine learning techniques for software imperfection predection. A thoughtful of quality viewpoints is pertinent for the software relationship to convey high software reliability. An accurate thought of metrics to forecast the quality characteristics is essential keeping in mind the end goal to procure understanding about the estimation of software in the primitive periods of software advancement and to guarantee restorative activities. We relate one measurable strategy and six machine learning system to anticipate the models. The proposed generation are approved utilizing dataset unruffled from Open Source software. The results are dissected utilizing Area under the Curve (AUC) accomplish from Receiver Operating Characteristics (ROC) testing. The results demonstrate that the imitation anticipated utilizing the arbitrary timberland and sacking strategies beat the various form. Henceforth, bolster on these results it is fair to guarantee that quality models have an impressive pertinence with Object Oriented metrics and that machine learning associations have a comparable execution with numerical strategies. It is test that the CBR routine utilizing the Mahalanobis separation likeness occupation additionally the reverse separation weighted arrangement calculation yielded the best fault prediction.Full Paper
机译：通过协调测试，可以准确预测代码中可能会发生错误的位置，从而降低成本并提高软件质量。本文的目标是研究模型的设置，所利用的自主因素以及相关的演示方法如何影响故障预测模型的执行。在本文中，我们指出了针对软件缺陷预测的各种机器学习技术的综合评估。对于软件关系，要传达出很高的软件可靠性，需要考虑周到的质量观点。牢记最终目标，以便在软件开发的原始阶段获得对软件估计的理解并保证恢复性活动，因此必须准确地考虑度量标准以预测质量特征。我们关联了一种可衡量的策略和六种机器学习系统来预测模型。利用从开源软件获得的数据集，批准了建议的生成。利用接收器工作特性（ROC）测试完成的曲线下面积（AUC）剖析结果。结果表明，利用任意林地和解雇策略预期的模仿击败了各种形式。从今以后，支持这些结果，可以公平地保证质量模型与面向对象的指标具有显着的相关性，并且机器学习关联在数值策略方面具有可比的执行力。测试证明，利用马氏距离相似度占用的CBR例程另外进行了反向分离加权排列计算，可以得出最佳的故障预测。

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