  Software quality assurance has been a heated topic for several decades. If
factors that influence software quality can be identified, they may provide
more insight for better software development management. More precise quality
assurance can be achieved by employing resources according to accurate quality
estimation at the early stages of a project. In this paper, a general procedure
is proposed to derive software quality estimation models and various techniques
are presented to accomplish the tasks in respective steps. Several statistical
techniques together with machine learning method are utilized to verify the
effectiveness of software metrics. Moreover, a neuro-fuzzy approach is adopted
to improve the accuracy of the estimation model. This procedure is carried out
based on data from the ISBSG repository to present its empirical value.
