  Early prediction of software quality is important for better software
planning and controlling. In early development phases, design complexity
metrics are considered as useful indicators of software testing effort and some
quality attributes. Although many studies investigate the relationship between
design complexity and cost and quality, it is unclear what we have learned
beyond the scope of individual studies. This paper presented a systematic
review on the influence of software complexity metrics on quality attributes.
We aggregated Spearman correlation coefficients from 59 different data sets
from 57 primary studies by a tailored meta-analysis approach. We found that
fault proneness and maintainability are most frequently investigated
attributes. Chidamber and Kemerer metric suite is most frequently used but not
all of them are good quality attribute indicators. Moreover, the impact of
these metrics is not different in proprietary and open source projects. The
result provides some implications for building quality model across project
type
