Prediction of Human Performance Capability during Software Development Using Classification

Abstract

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.The paper is organized as follows: Section 2 provides the related work and background of data mining Algorithms. Section 3 presents the research methodology to derive at a conclusion while Section 4 depicts the obtained result. Section 5 discusses the summary of this paper and its future scope.

A review of machine learning applications in human resource management

“…Use of ML in selection process has seen efforts towards identifying attributes to be used as selection criterion (Chien and Chen, 2008; Cho and Ngai, 2003; Gupta and Suma, 2014; Hu, 2017) and developing selection models accordingly (Chen and Chien, 2011; Tai and Hsu, 2006). Attributes identified range from employees' demographic characteristics like age, gender, marital status and past annual income to their personal characteristics like reaction capability, comprehensive ability and psychological quality.…”

“…Use of ML in selection process has seen efforts towards identifying attributes to be used as selection criterion (Chien and Chen, 2008;Cho and Ngai, 2003;Gupta and Suma, 2014;Hu, 2017) and developing selection models accordingly (Chen and Chien, 2011;Tai and Hsu, 2006)…”