The Confounding Effect of Class Size on The Validity of Obje(13)

de l’information

Associations between size and defects have been reported in non-object oriented systems [58]. For

object oriented programs, the relationship between size and defects is clearly visible in the study of [27],

where the Spearman correlation was found to be 0.759 and statistically significant. Another study of

image analysis programs written in C++ found a Spearman correlation of 0.53 between size in LOC and

the number of errors found during testing [55], and was statistically significant at an alpha level of 0.05.

Briand et al. [22] find statistically significant associations between 6 different size metrics and fault-

proneness for C++ programs, with a change in odds ratio going as high as 4.952 for one of the size

metrics.

General indications of a confounding effect are seen in Figure 3, which shows the associations between a

set of coupling metrics and fault-proneness, and with size from a recent study [22]. The association

between coupling metrics and fault-proneness is given in terms of the change in the odds ratio and the p-

value of the univariate logistic regression parameter. The association with size is in terms of the

Spearman correlation. As can be seen in Figure 3, all the metrics that had a significant relationship with

fault-proneness in the univariate analysis also had a significant correlation with size. Furthermore, there

is a general trend of increasing association between the coupling metric and fault-proneness as its

association with size increases.Relationship with fault-proneness

MetricChange in odds

Ratio

2.012

2.062

3.208

8.168

5.206

7.170

1.090

9.272

1.395

1.385

1.416

1.206

1.133

0.816

1.575

1.067

4.937

1.214p-value<0.0001<0.0001<0.0001<0.0001<0.0001<0.00010.5898<0.00010.03290.03890.03070.32130.33840.2520.09220.6735<0.00010.2737Relationship with sizerho0.32170.33590.39400.43100.32320.3168-0.1240.34550.17530.19580.12960.02970.0493-0.08550.2365-0.12290.2765-0.0345p-value<0.0001<0.0001<0.0001<0.0001<0.0001<0.00010.1082<0.00010.01630.00880.07850.70100.49130.25280.00190.11150.00010.6553CBOCBO’RFC1RFCMPCICPIH-ICPNIH-ICPDACDAC’OCAICFCAECOCMICOCMECIFMMICAMMICOMMICOMMEC

Figure 3: Relationship between coupling metrics and fault-proneness, and between coupling metrics and

size from [22]. This covers only coupling to non-library classes. This also excludes the following metrics

because no results pertaining to the relationship with fault-proneness were presented: ACAIC, DCAEC,

IFCMIC, ACMIC, IFCMEC, and DCMEC. The definition of these metrics is provided in the appendix.

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