Comparer des méthodes

	Analyse statique de code	Modèle de prédiction de défauts
Domaine	Génie logiciel	Génie logiciel
Famille	Process / pipeline	Process / pipeline
Année d'origine	2001	2005
Auteur d'origine	David Engler and William Pugh	Thomas Ostrand, Elaine Weyuker, Robert Bell
Type	automated analysis	machine learning model
Source fondatrice	Chess, B., & West, J. (2007). Secure Programming with Static Analysis. Addison-Wesley Professional.	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	static analysis, code inspection, automated review	fault prediction, bug prediction, defect classification
Apparentées	4	4
Résumé	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.	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.