Usporedite metode

	Statička analiza koda	Model za predviđanje defekata
Područje	Programsko inženjerstvo	Programsko inženjerstvo
Obitelj	Process / pipeline	Process / pipeline
Godina nastanka	2001	2005
Tvorac	David Engler and William Pugh	Thomas Ostrand, Elaine Weyuker, Robert Bell
Vrsta	automated analysis	machine learning model
Temeljni izvor	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.
Drugi nazivi	static analysis, code inspection, automated review	fault prediction, bug prediction, defect classification
Srodne	4	4
Sažetak	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.