摘要
软件故障静态预测通过从项目数据中提取度量信息预测故障,以便于测试和验证资源的分配。从可用度量数据和预测模型两个方面总结了软件故障静态预测方法,可用度量包括方法层、类层、构件层、文件层以及过程层度量,预测模型分为机器学习和统计方法两类;总结了性能评价指标、度量数据可得性以及故障分类对故障预测的影响等需要进一步研究的问题。
By predicting software faults with metrics information, the testing and verification resources are allocated effectively. Software fault static prediction methods are summarized from the point of view of available mettles data and predicting models. Available metrics data has method-level, class-level, component-level, file-level and protess-level categories. Predicting models have machine learning and statistics categories. Some issues to be studied are summarized, which are the performance assessing indexes, availability of the mettles and the effect of fault types on the fault prediction.
出处
《计算机科学与探索》
CSCD
2009年第5期449-459,共11页
Journal of Frontiers of Computer Science and Technology
基金
国家预研基金No.513270104~~
关键词
软件故障预测
软件度量
机器学习
统计方法
software fault prediction
software metrics
machine learning
statistics
