doi: 10.17586/2226-1494-2015-15-5-877-885


RISK MANAGEMENT AUTOMATION OF SOFTWARE PROJECTS BASED ОN FUZZY INFERENCE

T. M. Zubkova, E. N. Ishakova


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For citation: 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.

Abstract

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.  


Keywords: risk management, software design, program system, fuzzy inference, fuzzy logic, linguistic variables, knowledge base, expert evaluations.

Acknowledgements. The work was financially supported by the Russian Federal Property Fund and the Government of the Orenburg region (grant № 14-08-97031).

References
1. Titarenko B.P. Upravlenie Riskami v Innovatsionnykh Proektakh [Risk Management in Innovative Projects]. Moscow, MGSU Publ., 2011, 144 p.
2. Schmidt С., Dart P., Johnston L., Sterling L., Thorne P. Disincentives for communicating risk: a risk paradox. Information and Software Technology, 1999, vol. 41, no. 7, pp. 403–411. doi: 10.1016/S0950- 5849(99)00011-7
3. Tah J.H.M., Carr V. Towards a framework for project risk knowledge management in the construction supply chain. Advances in Engineering Software, 2001, vol. 32, no. 10–11, pp. 835–846. doi: 10.1016/S0965-9978(01)00035-7
4. Drew Procaccino J., Verner J.M., Overmyer S.P., Darter M.E. Case study: factors for early prediction of software development success. Information and Software Technology, 2002, vol. 44, no. 1, pp. 53–62. doi:
10.1016/S0950-5849(01)00217-8
5. Adler T.R., Leonard J.G., Nordgren R.K. Improving risk management: moving from risk elimination to risk avoidance. Information and Software Technology, 1999, vol. 41, no. 1, pp. 29–34.
6. Kudinov Y.I. Synthesis of a fuzzy-logic control system. Journal of Computer and Systems Sciences International, 1999, vol. 38, no. 1, pp. 158–164.
7. Chen J., Rine D.C. Training fuzzy logic controller software components by combining adaptation algorithms. Advances in Engineering Software, 2003, vol. 34, no. 3, pp. 125–137. doi: 10.1016/S0965- 9978(02)00140-0
8. Kandel A., Zhang Y.-Q., Henne M. On use of fuzzy logic technology in operating systems. Fuzzy Sets and Systems, 1998, vol. 99, no. 3, pp. 241–251.
9. Wang J. A fuzzy project scheduling approach to minimize schedule risk for product development. Fuzzy Sets and Systems, 2002, vol. 127, no. 2, pp. 99–116. doi: 10.1016/S0165-0114(01)00146-4
10. Carr V., Tah J.H.M. A fuzzy approach to construction project risk assessment and analysis: construction project risk management system. Advances in Engineering Software, 2001, vol. 32, no. 10–11, pp. 847–857.
doi: 10.1016/S0965-9978(01)00036-9
11. Ishakova E.N., Zubkova T.M., Medvedev A.S. Program system of the assessment of risks in the higher education area with the use of productional and frame model. Vestnik OSU, 2014, no. 1, pp. 183–188. (In
Russian)
12. DeMarco T., Lister T. Waltzing with Bears: Managing Risk on Software Projects. Dorset House, 2003, 144 p.
13. Gil'man D.V., Taganov A.I. Metodologicheskie Osnovy Analiza i Attestatsii Urovnei Zrelosti Protsessov Programmnykh Proektov v Usloviyakh Nechetkosti [Methodological Bases of the Analysis and Validation of Process Maturity Levels of Software Projects in Fuzziness Conditions]. Moscow, Goryachaya Liniya- Telekom Publ., 2014, 168 p.
14. Shtovba S.D. Obespechenie tochnosti i prozrachnosti nechetkoi modeli Mamdani pri obuchenii po eksperimental'nym dannym. Problemy Upravleniya i Informatiki, 2007, no. 4, pp. 102–114.
15. Zubkova T.M., Ishakova E.N., Mulyukov R.R. Programmnaya Sistema Intellektual'nogo Upravleniya Riskami Razrabotki Prikladnykh Programmnykh Izdelii. Certificate of State Registration of Computer Programs, no. 2014662317, 2014.


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