Comparer des méthodes

	Modèle de prédiction de défauts	Analyse statique de code
Domaine	Génie logiciel	Génie logiciel
Famille	Process / pipeline	Process / pipeline
Année d'origine	2005	2001
Auteur d'origine	Thomas Ostrand, Elaine Weyuker, Robert Bell	David Engler and William Pugh
Type	machine learning model	automated analysis
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.	Chess, B., & West, J. (2007). Secure Programming with Static Analysis. Addison-Wesley Professional.
Alias	fault prediction, bug prediction, defect classification	static analysis, code inspection, automated review
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.	Static code analysis automatically examines source code without execution, detecting potential bugs, security vulnerabilities, code smells, and style violations. Pioneered by Engler and Pugh (2001), automated analysis tools scan codebases at scale, identifying defect patterns faster than manual review. Organizations integrate static analysis into continuous integration pipelines to prevent defects early.