방법 비교
선택한 방법을 나란히 검토하세요. 서로 다른 행은 강조 표시됩니다.
| 등가 분할 테스팅× | 결함 예측 모델× | |
|---|---|---|
| 분야 | 소프트웨어공학 | 소프트웨어공학 |
| 계열 | Process / pipeline | Process / pipeline |
| 기원 연도≠ | 1979 | 2005 |
| 창시자≠ | Glenford Myers | Thomas Ostrand, Elaine Weyuker, Robert Bell |
| 유형≠ | partitioning strategy | machine learning model |
| 원전≠ | 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 ↗ |
| 별칭 | equivalence partitioning, BVA, boundary value analysis | fault prediction, bug prediction, defect classification |
| 관련 | 4 | 4 |
| 요약≠ | 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. |
| ScholarGate데이터셋 ↗ |
|
|