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A predictive approach to estimate software defects density using Probabilistic Neural Networks for the given Software Metrics

机译:对于给定的软件指标,使用概率神经网络估算软件缺陷密度的预测方法

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Software plays a very important role in our everyday life.There are many instances which show that even a small defect in the software can cause huge loss to many lives.By evaluating the errors in software in various phases,we can reduce the lateral cost of software.Software quality prediction has been an important arena since the las two decades. Several models and techniques have been proposed and utilized in this gaze.We can recognize the areas which are prone to hazards with the help of logic of quality prediction. In the proposed model, defect density indicator in requirement analysis, design, coding and testing phase is predicted using ten software metrics of these four phases.at the end of each phase the defect density indicator will be taken as an input for the next phase.With the help of ANN and PNN strategies,we have extnded our work. The experimental results are compared with fuzzy, ANN and PNN.Incomparision with ANN and fuzzy,the number of defects can be discovered better with ANN strategy......
机译:软件在我们的日常生活中起着非常重要的作用。许多实例表明,即使软件中的一个小缺陷也可能对许多人造成巨大损失。通过评估软件在各个阶段的错误,我们可以降低软件的横向成本。自从过去二十年来,软件质量预测一直是重要的领域。已经提出并利用了几种模型和技术。通过质量预测逻辑,我们可以识别出容易受到危害的区域。在提出的模型中,使用这四个阶段的十个软件指标来预测需求分析,设计,编码和测试阶段中的缺陷密度指标。在每个阶段结束时,缺陷密度指标将用作下一阶段的输入。借助ANN和PNN策略,我们扩展了我们的工作。将实验结果与模糊,人工神经网络和神经网络进行比较。与人工神经网络和模糊神经网络相比,利用人工神经网络策略可以更好地发现缺陷数量。

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