An enhanced software defect prediction model with multiple metrics and learners
An enhanced software defect prediction model with multiple metrics and learners
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具有多个指标和学习器的增强型软件缺陷预测模型
DOI：
10.1504/ijise.2016.074711
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发表时间：
2016-02
影响因子：
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通讯作者：
Li Zelin
中科院分区：
文献类型：
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作者：
Wang Shihai;He Ping;Li Zelin
缺陷预测是实现高可靠性软件的关键技术。基于软件度量的缺陷预测模型能够预测哪些模块容易出错，而哪些模块容易出错。预测结果将使软件开发人员更加关注这些高风险模块。对于软件缺陷预测建模，机器学习技术已经被广泛采用。模型选择问题一直是一个挑战，以产生一个有效的预测与满意的性能，这也总是难以实现。提出了一种基于多度量空间和多类型学习模型的软件缺陷预测建模框架。采用不同类型的构件分类器和不同的软件度量集，尽可能增加集成学习的多样性，构建软件缺陷预测集成模型。利用NASA MDP的一组真实的项目数据对该模型进行了充分的研究，实验结果表明，该模型有效地提高了泛化性能和预测精度。
Defect prediction is a critical technique for achieving high reliability software. Defect prediction models based on software metrics are able to predict which modules are fault-prone, which in turn. The prediction results would make the software developers to pay more attentions to these high-risk modules. For software defect prediction modelling, machine learning techniques have been widely employed. Model selection problem is always a challenge for generating an efficient predictor with a satisfied performance which is also always difficult to achieve. In this paper, a software defect prediction modelling framework based on multi-metric space and multi-type learning models is proposed. Different types of component classifiers and different software metric sets are used to build a software defect prediction ensemble model with the increment on the diversity of ensemble learning as far as possible. The proposed model is fully investigated by using a set of real project data from NASA MDP, the experimental results reveal that the model effectively improve the generalisation performance and the predictive accuracy.