Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Koda pārklājuma analīze× | Defektu prognozēšanas modelis× | |
|---|---|---|
| Nozare | Programmatūras inženierija | Programmatūras inženierija |
| Saime | Process / pipeline | Process / pipeline |
| Izcelsmes gads≠ | 1988 | 2005 |
| Autors≠ | Test Coverage Community | Thomas Ostrand, Elaine Weyuker, Robert Bell |
| Tips≠ | measurement and analysis | machine learning model |
| Pirmavots≠ | Zhu, H., Hall, P. A. V., & May, J. H. R. (1997). Software unit test coverage and adequacy. ACM Computing Surveys, 29(4), 366–427. DOI ↗ | 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. DOI ↗ |
| Citi nosaukumi | coverage metrics, test coverage, instrumentation-based measurement | fault prediction, bug prediction, defect classification |
| Saistītās | 4 | 4 |
| Kopsavilkums≠ | 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. | 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. |
| ScholarGateDatu kopa ↗ |
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