Comparer des méthodes

	Mesure de la dette technique	Modèle de prédiction de défauts
Domaine	Génie logiciel	Génie logiciel
Famille	Process / pipeline	Process / pipeline
Année d'origine	1992	2005
Auteur d'origine	Ward Cunningham	Thomas Ostrand, Elaine Weyuker, Robert Bell
Type	quantitative assessment	machine learning model
Source fondatrice	Cunningham, W. (1992). The WyCash Portfolio Management System. OOPSLA 92 Experience Report.	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	debt metrics, code health scoring, maintenance burden assessment	fault prediction, bug prediction, defect classification
Apparentées	4	4
Résumé	Technical debt represents accumulated shortcuts, deferred maintenance, and design compromises that incur future costs through slower development, higher defect rates, and deployment difficulty. Introduced by Ward Cunningham (1992), technical debt measurement quantifies these burdens using metrics like code complexity, duplication, test coverage gaps, and maintainability indices. Organizations use debt measurement to balance immediate delivery with long-term sustainability.	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.