Automatic Risk Identification in Software Projects: an Approach based on Inductive Learning

Julia Botan Machado, Silvio do Lago Pereira

Department of Information Technology, S?o Paulo State Technological College—FATEC/SP, S?o Paulo, Brazil.

DOI: 10.4236/iim.2012.425041                

Abstract

Effective risk management is very important to increase the probability of success in software projects. Indeed, like other types of projects, software projects are also susceptible to various problems that can lead to the cancelation of their development or to the development of systems that do not meet the client’s requirements. One of the main active- ties of risk management is the risk identification, because the list of risks generated in this activity is used all along the risk control process. Thus, this work proposes the creation of an expert system which is capable of identifying risks in software projects by using the lessons inductively learned from similar software projects already developed. By using this proposed expert system, project managers and software developers must be able to avoid errors of the past.

Keywords

Risk Management; Risk Identification; Software Engineering; Expert System

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	R. K. Wysocki, “Effective Project Management: Traditional, Agile, Extreme,” 5th Edition, John Wiley & Sons Ltd., Chichester, 2010.
[2]	C. A. R. Morano, C. G. Martins and M. L. R. Ferreira, “Application of Techniques for the Identification of Risk in the E & P Ventures,” Engevista, Vol. 8, No. 2, 2006, pp. 120-133.
[3]	H. P. Berger, “Risk Management: Procedures, Methods and Experiences,” Reliability: Theory & Applications, Vol. 2, No. 17, 2010, pp. 79-95.
[4]	J. R. Quinlan, “Induction of Decision Trees,” Machine Learning, Vol. 1, No. 1, 1985, pp. 81-106. doi:10.1007/BF00116251
[5]	A. Franco-Arcegaa, J. A. Carrasco-Ochoaa, G. Sánchez- Díazb and J. F. Martínez-Trinidada, “Decision Tree Induction Using a Fast Splitting Attribute Selection for Large Datasets,” Expert Systems with Applications, Vol. 38, No. 11, 2011, pp 14290-14300.
[6]	G. Jing and H. Zhidong, “New Decision Tree Algorithm with Restrained Factor Involved,” Physics Procedia, Vol. 25, 2012, pp. 1871-1878. doi:10.1016/j.phpro.2012.03.324
[7]	I. Sommerville, “Software Engineering,” 9th Edition, Addison-Wesley, Boston, 2010.
[8]	E. E. Odzaly and P. S. Des Greer, “Software Risk Management Barriers: An Empirical Study,” Proceedings of the 3rd International Symposium on Empirical Software Engineering and Measurement, Washington, 15-16 October 2009, pp. 418-421. doi:10.1109/ESEM.2009.5316014
[9]	J. Dhlamini, I. Nhamu and A. Kaihepa, “Intelligent Risk Management Tools for Software Development,” Proceedings of the Annual Conference of the Southern African Computer Lecturers’ Association, Eastern Cape, 2-11 July 2009, pp. 33-40.
[10]	PMI Standards Committee, “A Guide to the Project Management Body of Knowledge,” 4th Edition, Project Management Institute, 2008.
[11]	Y. H. Kwak and J. Stoddard, “Project Risk Management: Lessons Learned from Software Development,” Elsevier Science, Amsterdam, 2003. doi:10.1016/S0166-4972(03)00033-6
[12]	S. H. Liao, P. H. Chu and P. Y. Hsiao, “Data Mining Techniques and Applications—A Decade Review from 2000 to 2011,” Expert Systems with Applications, Vol. 39, No. 12, 2012, pp. 11303-11311. doi:10.1016/j.eswa.2012.02.063
[13]	S. H. Liao, “Methodologies and Applications—A Decade Review from 1995 to 2004,” Expert Systems with Applications, Vol. 28, No. 1, 2005, pp. 93-103. doi:10.1016/j.eswa.2004.08.003
[14]	S. Lucci and D. Kopec, “Artificial Intelligence in the 21st Century: A Living Introduction,” Mercury Learning and Information, Duxbury, 2012.