The Confounding Effect of Class Size on The Validity of Obje(4)

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the number of faults to investigate the validity of the metrics [57][10]. Also, univariate logistic regression

models are used as the basis for demonstrating the relationship between object-oriented product metrics

and fault-proneness in [22][19][106]. The importance of controlling for potential confounders in empirical

studies of object-oriented products has been emphasized [23]. However, size, the most obvious potential

confounder, has not been controlled in previous validation studies.

The objective of this paper is to investigate the confounding effect of class size on the validation of object-

oriented product metrics. We first demonstrate based on previous work that there is potentially a size

confounding effect in object-oriented metrics validation studies, and present a methodology for empirically

testing this. We then perform an empirical study on an object-oriented telecommunications framework5written in C++ [102]. The metrics we investigate consist of the CK metrics suite [30], and some of the

metrics defined by Lorenz and Kidd [80]. The external metric that we validate against is the occurrence of

a fault, which we term the fault-proneness of the class. In our study a fault is detected due to a field

failure.

Briefly, our results indicate that by using the commonly employed univariate analyses our results are

consistent with previous studies. After controlling for the confounding effect of class size, none of the

metrics is associated with fault-proneness. This indicates a strong confounding effect of class size on

some common object-oriented metrics. The results cast serious doubt that many previous validation

studies demonstrate more than that size is associated with fault-proneness.

Perhaps the most important practical implication of these results is that design and programming

guidelines based on previous validation studies are questioned. Efforts to control cost and quality using

object-oriented metrics as early indicators of problems may be achieved just as well using early indicators

of size. The implications for research are that data from previous validation studies should be re-

examined to gauge the impact of the size confounding effect, and future validation studies should control

for size.

In Section 2 we provide the rationale behind the confounding effect of class size and present a framework

for its empirical investigation. Section 3 presents our research method, and Section 4 includes the results

of the study. We conclude the paper in Section 5 with a summary and directions for future work.

2 Background

This section is divided in two parts. First, we present the theoretical and empirical basis of the object-

oriented metrics that we attempt to validate. Second, we demonstrate that there is a potentially strong

size confounding effect in object-oriented metrics validation studies.

2.1 Theoretical and Empirical Basis of Object-Oriented Metrics

2.1.1 Theoretical Basis and Its Empirical Support

The primary reason why there is an interest in the development of product metrics in general is

exemplified by the following justification for a product metric validity study “There is a clear intuitive basis

for believing that complex programs have more faults in them than simple programs” [87]. However, an

intuitive belief does not make a theory. In fact, the lack of a strong theoretical basis driving the

development of traditional software product metrics has been criticized in the past [68]. Specifically,

Kearney et al. [68] state that “One of the reasons that the development of software complexity measures

is so difficult is that programming behaviors are poorly understood. A behavior must be understood before

what makes it difficult can be determined. To clearly state what is to be measured, we need a theory of

programming that includes models of the program, the programmer, the programming environment, and

the programming task.” It has been stated that for historical reasons the CK metrics are the most referenced [23]. Most commercial metrics collection tools

available at the time of writing also collect these metrics.5

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