Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Defektu prognozēšanas modelis× | Programmatūras sarežģītības metriks× | |
|---|---|---|
| Nozare | Programmatūras inženierija | Programmatūras inženierija |
| Saime | Process / pipeline | Process / pipeline |
| Izcelsmes gads≠ | 2005 | 1976 |
| Autors≠ | Thomas Ostrand, Elaine Weyuker, Robert Bell | Thomas J. McCabe |
| Tips≠ | machine learning model | quantitative measurement |
| 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. DOI ↗ | McCabe, T. J. (1976). A complexity measure. IEEE Transactions on Software Engineering, 2(4), 308–320. DOI ↗ |
| Citi nosaukumi≠ | fault prediction, bug prediction, defect classification | code complexity analysis, complexity 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. | Software complexity metrics quantify the structural and operational difficulty of code through numerical measurements. Introduced by Thomas McCabe in 1976, cyclomatic complexity became the foundational approach. These metrics assess maintainability, testability, and defect risk, enabling teams to identify problematic code regions and guide refactoring efforts. |
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