Prediction of Human Performance Capability during Software Development Using Classification

Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 249)

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.

Keywords

software projects data mining selection criteria performance 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Han, J., Kamber, M.: Data Mining Concepts and Techniques, 2/e. Morgan Kaufmann Publishers, An imprint of Elsevier (2010)Google Scholar
  2. 2.
    Chien, C.F., Chen, L.F.: Data mining to improve personnel selection and enhance human capital: A case study in high-technology industry. Expert Systems and Applications 34, 280–290 (2008)CrossRefGoogle Scholar
  3. 3.
    Jantan, H., et al.: Human Talent Prediction in HRM using C4.5 Classification Algorithm. International Journal on Computer Science and Engineering (IJCSE) 2(8), 2526–2534 (2010)Google Scholar
  4. 4.
    Suma, V., Pushpavathi, T.P., Ramaswamy, V.: An Approach to Predict Software Project Success by Data Mining Clustering. In: International Conference on Data Mining and Computer Engineering (ICDMCE 2012), pp. 185–190Google Scholar
  5. 5.
    Singh, P.: Comparing the effectiveness of machine learning algorithms for defect prediction. International Journal of Information Technology and Knowledge Management, 481–483 (2009)Google Scholar
  6. 6.
    Quinlan, J.R.: Introduction of decision tree. Journal of Machine Learning, 81–106 (1986)Google Scholar
  7. 7.
    Witten, I., Frank, E., Hall, M.: Data Mining: Practical Machine Learning Tools and Techniques, 3rd edn. Morgan Kaufmann Publishers (2011)Google Scholar
  8. 8.
    Kusiak, A., Kern, J.A., Kernstine, K.H., Tseng, B.: Autonomous decision-making: A data mining approach. IEEE Trans. Inform. Technol. Biomedicine 4(4), 274–284 (2000)CrossRefGoogle Scholar
  9. 9.
    Chang, A.S., Leu, S.S.: Data mining model for identifying project profitability variables. International Journal of Project Management 24, 199–206 (2006)CrossRefGoogle Scholar
  10. 10.
    Gopalakrishnan Nair, T.R., Suma, V., Tiwari, P.K.: Analysis of Test Efficiency during Software Development Process. In: 2nd Annual International Conference on Software Engineering and Applications (SEA 2011) (2011)Google Scholar
  11. 11.
    Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, Inc. (1992)Google Scholar
  12. 12.
    Maimon, O., Rokach, L.: The Data Mining and Knowledge Discovery Handbook. Springer (2005)Google Scholar
  13. 13.
    Gupta, S., Suma, V.: Empirical study on selection of team members for software project- A data mining approach. International Journal of Computer Science and Informatics 3(2), 97–102 (2013) ISSN (PRINT): 2231 –5292Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Bangalore Dept. of CSEJain UniversityBangaloreIndia
  2. 2.RIICDayanada Sagar InstituteBangaloreIndia

Personalised recommendations