Software is the medium for implementing increasingly sophisticated features
during the maintenance phase as successive releases are developed. Softwar
e quality models can predict which software modules are likely to have faul
ts that will be discovered by customers, Such models are key components of
a system such as Enhanced Measurement for Early Risk Assessment of Latent D
efects (EMERALD), It is a sophisticated system of decision support tools us
ed by software designers and managers at Nortel to assess risk and improve
software quality and reliability of legacy software systems. This paper rep
orts an approach to software quality modelling that is suitable for industr
ial systems such as EMERALD.
We conducted a case study of a large legacy telecommunications system in th
e maintenance phase to predict whether each module will be considered fault
-prone. The case study is distinctive in the following respects, (1) Fault-
prone modules were defined in terms of faults discovered by customers, whic
h represent only a small fraction of the modules in the system. (2) We deve
loped models based on software product and process metrics that can make us
eful predictions at the end of the coding phase and at the time of release,
(3) The modelling approach is suitable for very large systems. We anticipa
te that refinements of this case study's models will be incorporated into E
MERALD. A similar approach could be taken for other systems. Copyright (C)
1999 John Wiley & Sons, Ltd.