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Comprehensive model for software fault prediction

机译：软件故障预测的综合模型

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摘要

Software Fault prediction (SFP) is an important task in the fields of software engineering to develop a cost effective software. Most of the software fault prediction is performed on same project date i.e., training and testing with same projects fault data. In case of unavailability of fault training data which is possible for the new project, data from the similar types/category of other projects can be used to train the model for the prediction. The software projects has been categorized into three categories by Boehm. The project within a certain group will be having good similarities with other projects within the group. So it is more suitable to train using the projects from same group. In this work we proposed to develop a model with similar category of data to predict the fault of another project belongs to same category. On basis of KLOC we have taken five organic software projects and performed various cross project and within project experiments. To generate a comprehensive generalized model for organic software's fault prediction, we have modeled various rule based to learner. Various rule-based learners used for comparison are JRip, CART, Conjunctive Rule, C4.5, NNge, OneR, Ridor, PART, and decision table-Naive Bayes hybrid classifier (DTNB).
机译：软件故障预测（SFP）是软件工程领域中开发具有成本效益的软件的重要任务。大多数软件故障预测是在相同的项目日期执行的，即使用相同的项目故障数据进行培训和测试。如果新项目可能无法使用故障训练数据，则可以使用其他项目的相似类型/类别的数据来训练预测模型。 Boehm已将软件项目分为三类。某个组中的项目将与该组中的其他项目具有良好的相似性。因此，更适合使用来自同一小组的项目进行培训。在这项工作中，我们建议开发一个数据类别相似的模型，以预测另一个属于同一类别的项目的故障。在KLOC的基础上，我们采取了五个有机软件项目，并在项目实验中执行了各种跨项目。为了生成用于有机软件故障预测的综合通用模型，我们为学习者建模了各种规则。用于比较的各种基于规则的学习器有JRip，CART，联合规则，C4.5，NNge，OneR，Ridor，PART和决策表-朴素贝叶斯混合分类器（DTNB）。

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