1. 1 740f02classsize18 The Confounding Effect of Class Size on the Validity of Object- Oriented Metrics Khaled El Eman, etal IEEE TOSE July 01

2. 2 740f02classsize18 Terminology u Confounding u Empirical u Binary classification u univariate u Type I error u Type II error u Odds ratio

3. 3 740f02classsize18 Odds Ratio

4. 4 740f02classsize18 Unmatched case-control study

5. 5 740f02classsize18 Size Confounding – fig 2

6. 6 740f02classsize18 Table 2 and 3

7. 7 740f02classsize18 Testing for confounding u Fig 3 u If association is not significant, assume no confounding – wrong

8. 8 740f02classsize18 Figure 3

9. 9 740f02classsize18 Testing for confounding u Do logistic regression u If the regression coefficient change dramatically with and without, then strong indication of confounding

10. 10 740f02classsize18 Figure 4

11. 11 740f02classsize18 Table 4

12. 12 740f02classsize18 Table 5

13. 13 740f02classsize18 Discussion “The results that we obtained are remarkably consistent.They indicate that, for some of the product metrics that we studied, the univariate analyses demonstrate that they are indeed associated with fault- proneness (namely, WMC, CBO, RFC, and NMA). After controlling for the size confounder, all of these associations disappear, and the differences in the product metrics' odds ratios indicates a strong and classic confounding effect. The remaining product metrics were not associated with fault-proneness from a univariate analysis, but this was predictable from previous work.” p642

14. 14 740f02classsize18 Conclusion u Table 5 u What is the conclusion of the article?

15. 15 740f02classsize18 Class Discussion u What kind of experiment should be used for these studies?