Salīdzināt metodes

	Defektu prognozēšanas modelis	Koda pārklājuma analīze
Nozare	Programmatūras inženierija	Programmatūras inženierija
Saime	Process / pipeline	Process / pipeline
Izcelsmes gads	2005	1988
Autors	Thomas Ostrand, Elaine Weyuker, Robert Bell	Test Coverage Community
Tips	machine learning model	measurement and analysis
Pirmavots	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.
Citi nosaukumi	fault prediction, bug prediction, defect classification	coverage metrics, test coverage, instrumentation-based measurement
Saistītās	4	4
Kopsavilkums	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.