Entropy based Bug Prediction using Neural Network based regression
Entropy based Bug Prediction using Neural Network based regression
复制标题
使用基于神经网络的回归进行基于熵的错误预测
DOI：
10.1109/ccaa.2015.7148399
复制
发表时间：
2015
期刊：
影响因子：
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通讯作者：
Deepti Chopra
中科院分区：
文献类型：
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作者：
Arvinder Kaur;Kamaldeep Kaur;Deepti Chopra
缺陷预测是软件工程领域的一个重要研究方向。研究人员已经开发并实现了许多错误预测方法，如过去的错误，代码搅动，重构，文件大小和作者数量等，并测量其性能。研究人员还提出了各种数学模型，用于监控错误检测和纠正过程。软件中引入的错误主要是因为软件代码中发生的连续更改。这些持续的变化往往会使代码变得复杂。通过熵量化的代码更改的复杂性用于预测错误。在以往的研究中，统计线性回归被用来构建缺陷预测模型。本文提出了一种基于熵的缺陷预测神经网络模型，并与SLR模型进行了比较。据观察，基于神经网络的回归（NNR）的性能优于或接近等于SLR。
Bug Prediction is an important research area in the field of software engineering. Researchers have developed and implemented a number of bug prediction approaches like past bugs, code churn, refactoring, file size and number of authors, etc and measured their performance. Various mathematical models have also been proposed by researchers for monitoring the bug detection and correction process. The bugs are introduced in the software mainly because of the continuous changes that occur in the software code. These continuous changes tend to make the code complex. The complexity of code changes, quantified by Entropy is used to predict bugs. In previous research, Statistical Linear Regression is used to construct bug prediction model. In this paper a Neural Network model of entropy based bug prediction is developed and compared with SLR model. It is observed that Neural Network based Regression (NNR) performs either better than or nearly equal to SLR.