Defektprædiktionsmodeller
Defektprædiktionsmodeller forudsiger sandsynligheden for softwarefejl i kodemoduler ved hjælp af statistiske metoder eller machine learning. Disse modeller, pioneret af Ostrand, Weyuker og Bell (2005), korrelerer kodemetrikker (kompleksitet, churn, kobling) med historiske defektdatasæt for at identificere komponenter med høj risiko. Organisationer anvender forudsigelserne til at allokere testressourcer, vejlede kodegennemgang og prioritere refaktorering.
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Method map
The neighbourhood of related methods — select a node to explore.
Kilder
- 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 ↗
Sådan citerer du denne side
ScholarGate. (2026, June 3). Software Defect Prediction and Risk Classification. ScholarGate. https://scholargate.app/da/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.
- Agil hastighedssporingSoftwareudvikling↔ compare
- KodedækningsanalyseSoftwareudvikling↔ compare
- SoftwarekompleksitetsmetrikkerSoftwareudvikling↔ compare
- Statisk kodeanalyseSoftwareudvikling↔ compare
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