Zubkova T.M., Ishakova E.N. Risk management automation of software projects based оn fuzzy inference. Scientific and Technical Journal of Information Technologies, Mechanics and Optics
, 2015, vol. 15, no. 5, pp. 877–885.
Application suitability for one of the intelligent methods for risk management of software projects has been shown based on the review of existing algorithms for fuzzy inference in the field of applied problems. Information sources in the management of software projects are analyzed; major and minor risks are highlighted. The most critical parameters have been singled out giving the possibility to estimate the occurrence of an adverse situations (project duration, the frequency of customer’s requirements changing, work deadlines, experience of developers’ participation in such projects and others.). The method of qualitative fuzzy description based on fuzzy logic has been developed for analysis of these parameters. Evaluation of possible situations and knowledge base formation rely on a survey of experts. The main limitations of existing automated systems have been identified in relation to their applicability to risk management in the software design. Theoretical research set the stage for software system that makes it possible to automate the risk management process for software projects. The developed software system automates the process of fuzzy inference in the following stages: rule base formation of the fuzzy inference systems, fuzzification of input variables, aggregation of sub-conditions, activation and accumulation of conclusions for fuzzy production rules, variables defuzzification. The result of risk management automation process in the software design is their quantitative and qualitative assessment and expert advice for their minimization. Practical significance of the work lies in the fact that implementation of the developed automated system gives the possibility for performance improvement of software projects.
risk management, software design, program system, fuzzy inference, fuzzy logic, linguistic variables, knowledge base, expert
The work was financially supported by the Russian Federal Property Fund and the Government of the Orenburg region (grant
№ 14-08-97031). References
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