So sánh phương pháp

	Mô hình dự đoán lỗi	Phân tích độ bao phủ mã nguồn
Lĩnh vực	Kỹ thuật phần mềm	Kỹ thuật phần mềm
Họ	Process / pipeline	Process / pipeline
Năm ra đời	2005	1988
Người khởi xướng	Thomas Ostrand, Elaine Weyuker, Robert Bell	Test Coverage Community
Loại	machine learning model	measurement and analysis
Công trình gốc	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.
Tên gọi khác	fault prediction, bug prediction, defect classification	coverage metrics, test coverage, instrumentation-based measurement
Liên quan	4	4
Tóm tắt	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.