The Confounding Effect of Class Size on The Validity of Obje(7)

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2.1.2.1 WMC

This is the Weighted Methods per Class metric [30], and can be classified as a traditional complexity

metric. It is a count of the methods in a class. The developers of this metric leave the weighting scheme

as an implementation decision [30]. We weight it using cyclomatic complexity as did [78]. However, other

authors did not adopt a weighting scheme [4][106]. Methods from ancestor classes are not counted and

neither are “friends” in C++. This is similar to the approach taken in, for example, [4][31]. To be precise,8WMC was counted after preprocessing to avoid undercounts due to macros [33].

One study found WMC to be associated with fault-proneness on three different sub-systems written in9C++ with p-values 0.054, 0.0219 and 0.0602, and change in odds ratio 1.26, 1.45, and 1.26 [106]. A

study that evaluated WMC on a C++ application and a Java application found WMC to have a Spearman

correlation of 0.414 and 0.456 with the number of faults due to field failures respectively, and highly

significant p-values (<0.0001 and <0.0056) [10]. Another study using student systems found WMC to be10associated with fault-proneness with a p-value for the logistic regression coefficient of 0.0607 [4].

2.1.2.2 DIT

The Depth of Inheritance Tree [30] metric is defined as the length of the longest path from the class to the

root in the inheritance hierarchy. It is stated that as one goes further down the class hierarchy the more

complex a class becomes, and hence more fault-prone.

The DIT metric was empirically evaluated in [19][22]. In [19] the authors found that this metric was related

to fault-proneness (p=0.0074) with a change in odds ratio equal to 0.572 when measured on non-library

classes. The second study [22] also found it to be associated with fault-proneness (p=0.0001) with a

change in odds ratio of 2.311. Another study using student systems found DIT to be associated with fault-

proneness with a p-value for the logistic regression coefficient <0.0001 [4].

It will be noted that in the first study a negative association was found between DIT and fault-proneness.

The authors explain this by stating that in the system studied classes located deeper in the inheritance

hierarchy provide only implementations for a few specialized methods, and are therefore less likely to

contain faults than classes closer to the root [19]. This was a deliberate strategy to place as much

functionality as close as possible to the root of the inheritance tree. Note that for the latter two

investigations, the same data set was used, and therefore the slightly different coefficients may have

been due to removal of outliers.

One study using data from an industrial system found that classes involved in an inheritance structure

were more likely to have defects (found during integration testing and within 12 months post-delivery)

[27]. Another study did not find DIT to be associated with fault-proneness on three different sub-systems

written in C++, where faults were based on three years’ worth of trouble reports [106]. One study that

evaluated DIT on a Java application found that it had a Spearman correlation of 0.523 (p<0.0015) with the

number of faults due to field failures [10].

2.1.2.3 NOC

This is the Number of Children inheritance metric [30]. This metric counts the number of classes which

inherit from a particular class (i.e., the number of classes in the inheritance tree down from a class).

The NOC metric was empirically evaluated in [19][22]. In [19] the authors found that this metric was not

related to fault-proneness. Conversely, the second study [22] found it to be associated with fault-

proneness (p=0.0276) with a change in odds ratio of 0.322. Another study using student systems found8 Note that macros embodied in #ifdef’s are used to customize the implementation to a particular platform. Therefore, the method is

defined at design time but its implementation is conditional on environment variables. Not counting it, as suggested in [31], would

undercount methods known at design time.

In this study faults were classified as either object-oriented type faults or traditional faults. The values presented here are for all of

the faults, although the same metrics were found to be significanct for both all faults and the object-oriented only faults.

Furthermore, the change in odds ratio reported is based on a change of one unit of the metric rather than a change in the standard

deviation.

109 This study used the same data set as in [22], except that the data was divided into subsets using different criteria. The results

presented here are for all of the classes.

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