The Confounding Effect of Class Size on The Validity of Obje(14)

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This leads us to conclude that, potentially, previous validation studies have overestimated the impact of

object oriented metrics on fault-proneness due to the confounding effect of size.

2.3 Summary

In this section the theoretical basis for object-oriented product metrics was presented. This states that

cognitive complexity is an intervening variable between the structural properties of classes and fault-

proneness. Furthermore, the empirical evidence supporting the validity of the object oriented metrics that

we study was presented, and this indicates that some of the metrics are strongly associated with fault-

proneness or the number of faults. We have also demonstrated that there is potentially a strong size

confounding effect in empirical studies to date that validate object oriented product metrics. This makes it

of paramount importance to determine whether such a strong confounding effect really exists.

If a size confounding effect is found, this means that previous validation studies have a positive bias and

may have exaggerated the impact of product metrics on fault-proneness. The reason is that studies to

date relied exclusively on univariate analysis to test the hypothesis that the product metrics are

associated with fault-proneness or the number of faults. The objective of the study below then is to

directly test the existence of this confounding effect and its magnitude.

3 Research Method

3.1 Data Source

Our data set comes from a telecommunications framework written in C++ [102]. The framework

implements many core design patterns for concurrent communication software. The communication

software tasks provided by this framework include event demultiplexing and event handler dispatching,

signal handling, service initialization, interprocess communication, shared memory management,

message routing, dynamic (re)configuration of distributed services, and concurrent execution and

synchronization. The framework has been used in applications such as electronic medical imaging

systems, configurable telecommunications systems, high-performance real-time CORBA, and web

servers. Examples of its application include in the Motorola Iridium global personal communications

system [101] and in network monitoring applications for telecommunications switches at Ericsson [100]. A

total of 174 classes from the framework that were being reused in the development of commercial

switching software constitute the system that we study. A total of 14 different programmers were involved16in the development of this set of classes.

3.2 Measurement

3.2.1 Product Metrics

All product metrics are defined on the class, and constitute design metrics, and they have been presented

in Section 2.1.2. In our study the size variable was measured as non-comment source LOC for the class.

Measurement of product metrics used a commercial metrics collection tool that is currently being used by

a number of large telecommunications software development organizations.

3.2.2 Dependent Variable

17For this product, we obtained data on the faults found in the library from actual field usage. Each fault

was due to a unique field failure and represents a defect in the program that caused the failure. Failures

were reported by the users of the framework. The developers of the framework documented the reasons

for each delta in the version control system, and it was from this that we extracted information on whether

a class was faulty.16

17 This number was obtained from the different login names of the version control system associated with each class. It has been argued that considering faults causing field failures is a more important question to address than faults found during

testing [9]. In fact, it has been argued that it is the ultimate aim of quality modeling to predict post-release fault-proness [50]. In at

least one study it was found that pre-release fault-proneness is not a good surrogate measure for post-release fault-proness, the

reason posited being that pre-release fault-proness is a function of testing effort [51].

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