Comparer des méthodes

	Modèles de fiabilité logicielle	Modèle de prédiction de défauts
Domaine	Génie logiciel	Génie logiciel
Famille	Process / pipeline	Process / pipeline
Année d'origine	1979	2005
Auteur d'origine	Alok Goel and Kazuhira Okumoto	Thomas Ostrand, Elaine Weyuker, Robert Bell
Type	stochastic model	machine learning model
Source fondatrice	Goel, A. L., & Okumoto, K. (1979). Time-dependent error-detection rate model for software reliability and other performance measures. IEEE Transactions on Reliability, 28(3), 206–211.	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.
Alias	reliability growth model, failure rate prediction, SRGM	fault prediction, bug prediction, defect classification
Apparentées	4	4
Résumé	Software reliability models predict the behavior of failure rates during testing and operation, estimating when software achieves required reliability targets. Introduced by Goel and Okumoto (1979), these stochastic models capture how defect discovery declines as testing progresses. Organizations use reliability models to forecast release readiness, estimate testing duration, and validate quality achievement.	Defect prediction models forecast the likelihood of software faults in code modules using statistical or machine learning approaches. Pioneered by Ostrand, Weyuker, and Bell (2005), these models correlate code metrics (complexity, churn, coupling) with historical defect data to identify high-risk components. Organizations use predictions to allocate testing resources, guide code review, and prioritize refactoring.