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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.

has been cited by the following article:

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

  AUTHORS: Julia Botan Machado, Silvio do Lago Pereira

  KEYWORDS: Risk Management; Risk Identification; Software Engineering; Expert System

  JOURNAL NAME: Intelligent Information Management, Vol.4 No.5A, October 30, 2012

  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.