Advertisement

Classification of Software Project Risk Factors Using Machine Learning Approach

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 385)

Abstract

Software project risk can be defined as a various future harms that could be possible on the software due to some non-noticeable mistakes done during the development of software project. Analyzing the risk is required in order to reduce the risk before it can harm the quality of the project. This paper interprets an idea of software project risk factors classification which involves the use of support vector machines (SVM) i.e., machine learning approach to improve the accuracy of the results. Risk assessment is a crucial task as the projects are facing increased complexity with higher uncertainties. In order to make the risk assessment easier, it is necessary for the developers to identify the hardbound and less hardbound risk factors. Classifying the risk factors will help the developers to identify the most effective risk which will ultimately become easy for the software developer to take some mitigation actions as early as possible. Hence the proposed approach reduces the developer’s effort and increases the accuracy in identifying the harmful risk factors.

Keywords

Analytical Hierarchy Process (AHP) Support Vector Machines (SVM) Risk Factors WEKA Tool 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Chittister, C., Haimes, Y.Y.: Risk associated with software development: a bolistic framework for assessment and management. IEEE Transaction on Systems, Man and Cybernetics 23, 710–723 (1993)CrossRefGoogle Scholar
  2. 2.
    Boehm, B.: Software risk management. Springer (1989)Google Scholar
  3. 3.
    Vapnik, V.N.: The Nature of Statistical Learning Theory. Springer, New York (1995)CrossRefMATHGoogle Scholar
  4. 4.
    Cortes, C., Vapnik, V.: Support-vector networks. Machine Learning 20, 273–297 (1995)MATHGoogle Scholar
  5. 5.
    Burges, J.C.: A Tutorial on Support Vector Machines for Pattern Recognition. Data Mining and Knowledge Discovery 2, 121–167 (1998)CrossRefGoogle Scholar
  6. 6.
    Chang, C-C., Lin, C-J.: LIBSVM: A Library for Support Vector Machines, March 2013Google Scholar
  7. 7.
    Wallace, L., Keil, M., Rai, A.: Understanding Software Project Risk: A Cluster Analysis. Information Management 42, 115–125 (2004)CrossRefGoogle Scholar
  8. 8.
    Feng, N., Li, M., Gao, H.: A software project risk analysis model based on evidential reasoning approach. In: World Congress on Software Engineering (2009)Google Scholar
  9. 9.
    Wulan, M., Petrovic, D.: A Fuzzy Logic Based System for Risk Analysis and Evaluation within Enterprise Collaborations. Computers in Industry 63, 739–748 (2012)CrossRefGoogle Scholar
  10. 10.
    Fang, C., Marle, F.: A Simulation-Based Risk Network Model for Decision Support in Project Risk Management. Decision Support Systems 52, 635–644 (2012)CrossRefGoogle Scholar
  11. 11.
    Singh, D., Sharma, A.: Software requirement prioritization using machine learning. In: Proceedings of 26th International Conference on Software Engineering & Knowledge Engineering, SEKE-2014, Vancouver, Canada, pp. 701–704, July 2014Google Scholar
  12. 12.
    Gunn, S.: Support Vector Machines for Classification and Regression, ISIS Technical Report, November 1997Google Scholar
  13. 13.
    Goepel, K.D.: BPMSG’s AHP Online System, BPMSG Online System, May 2014Google Scholar
  14. 14.
    Palcic, I., Lalic, B.: Analytical Hierarchy Process as a Tool for Selecting and Evaluating Projects. Int. J. Simul. Model 8, 16–26 (2009)CrossRefGoogle Scholar
  15. 15.
    Saaty, T.L.: Fundamentals of Analytical Hierarchy Process. RWS Publications (1994)Google Scholar
  16. 16.
    Sharma, T.C., Jain, M.: WEKA Approach for Comparative Study of Classification Algorithm. International Journal of Advanced Research in Computer and Communication Engineering 2, April 2013Google Scholar
  17. 17.
    Jayenthi, S.N.: Efficient Classification Algorithms using SVMs for Large Datasets. Supercomputer Education and Research Center, June 2007Google Scholar
  18. 18.
    Suh, S.D.: Risk Management in a Large-Scale New Railway Transport System Project, June 2000Google Scholar
  19. 19.
    Chu, K.-K., Li, C.-H.: A Study of The Effect of Risk- Reduction Strategies on Purchase Intentions in Online Shopping. International Journal of Electronic Business Management 6, 213–226 (2008)Google Scholar
  20. 20.

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Prerna Chaudhary
    • 1
  • Deepali Singh
    • 1
  • Ashish Sharma
    • 1
  1. 1.GLA UniversityMathuraIndia

Personalised recommendations