  BACKGROUND: In object oriented (OO) software systems, class size has been
acknowledged as having an indirect effect on the relationship between certain
artifact characteristics, captured via metrics, and faultproneness, and
therefore it is recommended to control for size when designing fault prediction
models. AIM: To use robust statistical methods to assess whether there is
evidence of any true effect of class size on fault prediction models. METHOD:
We examine the potential mediation and moderation effects of class size on the
relationships between OO metrics and number of faults. We employ regression
analysis and bootstrapping-based methods to investigate the mediation and
moderation effects in two widely-used datasets comprising seventeen systems.
RESULTS: We find no strong evidence of a significant mediation or moderation
effect of class size on the relationships between OO metrics and faults. In
particular, size appears to have a more significant mediation effect on CBO and
Fan-out than other metrics, although the evidence is not consistent in all
examined systems. On the other hand, size does appear to have a significant
moderation effect on WMC and CBO in most of the systems examined. Again, the
evidence provided is not consistent across all examined systems CONCLUSION: We
are unable to confirm if class size has a significant mediation or moderation
effect on the relationships between OO metrics and the number of faults. We
contend that class size does not fully explain the relationships between OO
metrics and the number of faults, and it does not always affect the
strength/magnitude of these relationships. We recommend that researchers
consider the potential mediation and moderation effect of class size when
building their prediction models, but this should be examined independently for
each system.
