  The quality of human capital is crucial for software companies to maintain
competitive advantages in knowledge economy era. Software companies recognize
superior talent as a business advantage. They increasingly recognize the
critical linkage between effective talent and business success. However,
software companies suffering from high turnover rates often find it hard to
recruit the right talents. There is an urgent need to develop a personnel
selection mechanism to find the talents who are the most suitable for their
software projects. Data mining techniques assures exploring the information
from the historical projects depending on which the project manager can make
decisions for producing high quality software. This study aims to fill the gap
by developing a data mining framework based on decision tree and association
rules to refocus on criteria for personnel selection. An empirical study was
conducted in a software company to support their hiring decision for project
members. The results demonstrated that there is a need to refocus on selection
criteria for quality objectives. Better selection criteria was identified by
patterns obtained from data mining models by integrating knowledge from
software project database and authors research techniques.
