Uporedite metode

	Model za predviđanje defekata	Анализа покривености кода
Oblast	Softversko inženjerstvo	Softversko inženjerstvo
Porodica	Process / pipeline	Process / pipeline
Godina nastanka	2005	1988
Tvorac	Thomas Ostrand, Elaine Weyuker, Robert Bell	Test Coverage Community
Tip	machine learning model	measurement and analysis
Temeljni izvor	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.	Zhu, H., Hall, P. A. V., & May, J. H. R. (1997). Software unit test coverage and adequacy. ACM Computing Surveys, 29(4), 366–427.
Drugi nazivi	fault prediction, bug prediction, defect classification	coverage metrics, test coverage, instrumentation-based measurement
Srodne	4	4
Sažetak	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.	Code coverage analysis measures the extent to which source code is executed by a test suite, quantifying which lines, branches, or paths are exercised. Tools instrument code to track execution, reporting coverage percentages and identifying untested regions. Coverage analysis guides test creation, detects dead code, and validates test adequacy in quality assurance processes.