Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Detección de olores arquitectónicos× | Modelo de Predicción de Defectos× | |
|---|---|---|
| Campo | Ingeniería de software | Ingeniería de software |
| Familia | Process / pipeline | Process / pipeline |
| Año de origen≠ | 2009 | 2005 |
| Autor original≠ | Martin Fowler and García et al. | Thomas Ostrand, Elaine Weyuker, Robert Bell |
| Tipo≠ | pattern-based analysis | machine learning model |
| Fuente seminal≠ | Fowler, M. (2018). Code smell. Martin Fowler's Website. 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 | design smell detection, architectural debt analysis, system quality assessment | fault prediction, bug prediction, defect classification |
| Relacionados | 4 | 4 |
| Resumen≠ | Architecture smells are recurring patterns in system structure that indicate potential design problems. Introduced by García et al. (2009), these patterns signal violations of architectural principles (modularity, independence, abstraction) at system scale. Detection combines code metrics, dependency analysis, and pattern recognition to identify smells early, guiding refactoring and architectural improvements. | 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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