Prediction of Human Performance Capability during Software Development using Classification
This addresses recruitment inefficiencies for software companies, but it is incremental as it applies existing data mining methods to a specific domain.
The study tackled the problem of high turnover and recruitment challenges in software companies by developing a data mining framework using decision trees and association rules to improve personnel selection criteria, with an empirical test in a company supporting hiring decisions.
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.