Zero-inflated prediction model in software-fault data

Fagundes Roberta A.A.; Souza Renata M.C.R.; Cysneiros Francisco J.A.

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Zero-inflated prediction model in software-fault data

机译：软件故障数据中的零膨胀预测模型

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Software fault data with many zeroes in addition to large non-zero values are common in the software estimation area. A two-component prediction approach that provides a robust way to predict this type of data is introduced in this study. This approach allows to combine parametric and non-parametric models to improve the prediction accuracy. This way provides a more flexible structure to understand data. To show the usefulness of the proposed approach, experiments using eight projects from the NASA repository are considered. In addition, this method is compared with methods from the machine learning and statistical literature. The performance of the methods is measured by the prediction accuracy that is assessed based on the mean magnitude of relative errors.

机译：除了较大的非零值外，具有许多零的软件故障数据在软件估计区域中很常见。本研究介绍了一种两成分预测方法，该方法提供了一种可靠的方法来预测此类数据。这种方法允许组合参数模型和非参数模型以提高预测精度。这种方式提供了一种更灵活的结构来理解数据。为了显示该方法的有效性，考虑了使用来自NASA信息库的八个项目进行的实验。此外，将该方法与机器学习和统计文献中的方法进行了比较。通过基于相对误差的平均幅度评估的预测准确性来衡量方法的性能。

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《Software, IET》 |2016年第1期|1-9|共9页
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Fagundes Roberta A.A.; Souza Renata M.C.R.; Cysneiros Francisco J.A.;
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Pernambuco University, Brazil;

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1. Modelling count data with excessive zeros: the need for class prediction in zero-inflated models and the issue of data generation in choosing between zero-inflated and generic mixture models for dental caries data. [J] . Gilthorpe MS, Frydenberg M, Cheng Y Statistics in medicine . 2009,第28期

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2. Analysis of joint modeling of longitudinal zero-inflated power series and zero-inflated time to event data [J] . Zeinali Najafabadi Mojtaba, Bahrami Samani Ehsan, Ganjali Mojtaba Journal of biopharmaceutical statistics . 2020,第5期

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4. Extreme Value Prediction for Zero-Inflated Data [C] . Fan Xin, Zubin Abraham Pacific-Asia conference on advances in knowledge discovery and data mining;PAKDD 2012 . 2012

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Zero-inflated prediction model in software-fault data

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