Comparer des méthodes

	Test de partitionnement par équivalence	Modèle de prédiction de défauts
Domaine	Génie logiciel	Génie logiciel
Famille	Process / pipeline	Process / pipeline
Année d'origine	1979	2005
Auteur d'origine	Glenford Myers	Thomas Ostrand, Elaine Weyuker, Robert Bell
Type	partitioning strategy	machine learning model
Source fondatrice	Myers, G. J. (1979). The Art of Software Testing. John Wiley & Sons.	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	equivalence partitioning, BVA, boundary value analysis	fault prediction, bug prediction, defect classification
Apparentées	4	4
Résumé	Equivalence partitioning divides input domains into equivalence classes—sets of inputs expected to behave identically—then selects test cases from each class. Introduced by Myers (1979), this technique reduces test cases while maintaining effectiveness. Boundary value analysis (BVA) complements partitioning by testing values at partition boundaries where failures often occur.	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.