Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Métricas de Complejidad del Software× | Modelo de Predicción de Defectos× | |
|---|---|---|
| Campo | Ingeniería de software | Ingeniería de software |
| Familia | Process / pipeline | Process / pipeline |
| Año de origen≠ | 1976 | 2005 |
| Autor original≠ | Thomas J. McCabe | Thomas Ostrand, Elaine Weyuker, Robert Bell |
| Tipo≠ | quantitative measurement | machine learning model |
| Fuente seminal≠ | McCabe, T. J. (1976). A complexity measure. IEEE Transactions on Software Engineering, 2(4), 308–320. 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 ↗ |
| Alias≠ | code complexity analysis, complexity measurement | fault prediction, bug prediction, defect classification |
| Relacionados | 4 | 4 |
| Resumen≠ | 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. | 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. |
| ScholarGateConjunto de datos ↗ |
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