Process / pipelineQuality prediction
缺陷预测模型
缺陷预测模型使用统计学或机器学习方法预测代码模块中出现软件故障的可能性。该方法由 Ostrand、Weyuker 和 Bell (2005) 开创，通过将代码度量（复杂度、变更量、耦合度）与历史缺陷数据相关联，来识别高风险组件。组织利用这些预测来分配测试资源、指导代码审查和优先处理重构工作。
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来源
- Ostrand, T. J., Weyuker, E. J., & Bell, R. M. (2005). Predicting the location and number of faults in large software systems. IEEE Transactions on Software Engineering, 31(4), 340–355. DOI: 10.1109/tse.2005.49 ↗
- Nagappan, N., Ball, T., & Zeller, A. (2006). Mining metrics to predict component failures. In Proceedings of the 28th International Conference on Software Engineering (pp. 452–461). DOI: 10.1145/1134285.1134349 ↗
- Menzies, T., Greenwald, J., & Russ, P. (2007). Problems with precision: A response to comments on 'Data mining static code attributes to learn defect predictors'. IEEE Transactions on Software Engineering, 33(9), 637–640. DOI: 10.1109/tse.2007.70721 ↗
如何引用本页
ScholarGate. (2026, June 3). Software Defect Prediction and Risk Classification. ScholarGate. https://scholargate.app/zh/software-engineering/defect-prediction-model
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
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