Comparer des méthodes

	Modèle de prédiction de défauts	Métriques de complexité logicielle
Domaine	Génie logiciel	Génie logiciel
Famille	Process / pipeline	Process / pipeline
Année d'origine	2005	1976
Auteur d'origine	Thomas Ostrand, Elaine Weyuker, Robert Bell	Thomas J. McCabe
Type	machine learning model	quantitative measurement
Source fondatrice	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.	McCabe, T. J. (1976). A complexity measure. IEEE Transactions on Software Engineering, 2(4), 308–320.
Alias	fault prediction, bug prediction, defect classification	code complexity analysis, complexity measurement
Apparentées	4	4
Résumé	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.	Software complexity metrics quantify the structural and operational difficulty of code through numerical measurements. Introduced by Thomas McCabe in 1976, cyclomatic complexity became the foundational approach. These metrics assess maintainability, testability, and defect risk, enabling teams to identify problematic code regions and guide refactoring efforts.