Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Particionamiento por equivalencia× | Modelo de Predicción de Defectos× | |
|---|---|---|
| Campo | Ingeniería de software | Ingeniería de software |
| Familia | Process / pipeline | Process / pipeline |
| Año de origen≠ | 1979 | 2005 |
| Autor original≠ | Glenford Myers | Thomas Ostrand, Elaine Weyuker, Robert Bell |
| Tipo≠ | partitioning strategy | machine learning model |
| Fuente seminal≠ | Myers, G. J. (1979). The Art of Software Testing. John Wiley & Sons. link ↗ | 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 | equivalence partitioning, BVA, boundary value analysis | fault prediction, bug prediction, defect classification |
| Relacionados | 4 | 4 |
| Resumen≠ | Equivalence partitioning divides input domains into equivalence classes—sets of inputs expected to behave identically—then selects test cases from each class. Introduced by Myers (1979), this technique reduces test cases while maintaining effectiveness. Boundary value analysis (BVA) complements partitioning by testing values at partition boundaries where failures often occur. | 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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