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The Way of Quality Management of the Decision Making Software Systems Development

  • O. N. Dolinina
  • V. A. Kushnikov
  • V. V. Pechenkin
  • A. F. Rezchikov
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 763)

Abstract

The different characteristics of the decision making system’s software quality are analyzed. In spite of a lot of research comprehensive criterion of the software quality management still exists only on an informal level. There are described the differences between Russian GOST R standard and ISO. It is shown that the quality of the software is a manageable indicator can be represented by an acyclic connected graph G, in which the upper level is represented by the following characteristics according to the standard ISO. The task of the providing of the planned quality level is formalized as the optimization one taking into consideration the vectors of the control activities and environment states. Special attention is given to the quality characteristics of the intellectual systems. Plan of the activities is validated by the Boolean functions, for this aim graph of the causal relationships is built and transferred to the logic scheme. The plan can be built at any stage of the software life cycle.

Keywords

Integrated criteria of the software quality Optimization task Vectors of the control activities Graph of the causal relationships Discrete logic scheme 

References

  1. 1.
    Avizienis, A., Laprie, J.-C., Randell, B., Landwehr, C.: Basic concepts and taxonomy of dependable and secure computing. IEEE Trans. Dependable Secure Comput. 1, 1–33 (2004)CrossRefGoogle Scholar
  2. 2.
    Lozinsky, A., Shubinsky, I.: Defining requirements to software (in Russian). http://www.ibtrans.ru/Requirements.pdf. Accessed 06 May 2016
  3. 3.
    Klark, E., Gramberg, O., Peled, D.: Verification of Program Models: Model Checking. MTSNMO, Moscow (2002). (in Russian)Google Scholar
  4. 4.
    Mayers, G.: Quality of software. Mir, Moscow (1980). (in Russian)Google Scholar
  5. 5.
    Lipayev, V.: Methods of quality assurance for large-scale software (In Russian). Sinteg, Moscow (2003). (in Russian)Google Scholar
  6. 6.
    Kaner, K., Folk, D., Nguyen, E.: Software testing. Fundamentals of business application management. DiaSoft, Kyiv (2001). (in Russian)Google Scholar
  7. 7.
    Crispin, L., Gregory, J.: Agile Testing: A Practical Guide for Testers and Agile Teams. Vilyams, Moscow (2010). (in Russian)Google Scholar
  8. 8.
    Beyzer, B.: Testing the Black Box. Technologies for Functional Testing of Software and Systems. Piter, St. Petersburg (2004). (in Russian)Google Scholar
  9. 9.
    Culbertson, R., Brown, C., Cobb, G.: Rapid Testing. Vilyams, Moscow (2002)Google Scholar
  10. 10.
    ISO/IEC 25010:2011 Systems and software engineering – Systems and software Quality Requirements and Evaluation (SQuaRE) – System and software quality models. https://www.icc-iso.ru/toclients/standard
  11. 11.
    Shlychkov, E., Pokhaznikov, M., Kushnikov, V., Kalashnikova, O.: Feasibility analysis of the action plans in operating control of the machine engineering factory. Vestnik Saratovskogo gosudarstvennogo tekhnicheskogo universiteta 1(1–21), 88–95 (2007). (in Russian)Google Scholar
  12. 12.
    Sklyomin, A., Kushnikov, V., Rezchikov, A.: Models and algorithms of feasibility analysis of the action plans in industry enterprise management. Vestnik Saratovskogo gosudarstvennogo tekhnicheskogo universiteta 3(1–67), 145–152 (2012). (In Russian)Google Scholar
  13. 13.
    Dolinina, O.: Method of the debugging of the knowledge bases of intellectual decision making systems. In: Automation Control Theory Perspectives in Intelligent Systems, Proc. of the 5th Computer Science Conference 2016 (CSOC 2016), vol. 3, pp. 307–315. Springer (2016)CrossRefGoogle Scholar
  14. 14.
    Dolinina, O., Rezchikov, A., Suchkova, N.: Formal models of structural errors in intellectual systems’ knowledge bases. Sovremennye naukoyomkie tekhnologii, vol. 3 (2017), http://www.top-technologies.ru/ru/article/view?id=36607. Accessed 13 Apr 2017
  15. 15.
    Ginsberg, A.: Knowledgebase reduction: a new approach to checking knowledge bases for inconsistency & redundancy. In: Proceedings of 7th National Conference on Artificial Intelligence (AAAI 1988) (St. Paul MN), vol. 2, pp. 585–589 (1998)Google Scholar
  16. 16.
    El-Korany, A., Shaalan, K., Baraka, H., Rafea, A.: An approach for automating the verification of KADS-based expert systems. New Rev. Appl. Expert Syst. 4, 107–124 (1998)Google Scholar
  17. 17.
    Avramov, M., Antropov, P., Gubin, N., Dolinina, O., et al.: Expert system of fault diagnosis of gas pumping units at compressor stations. Intellektualnye sistemy v proizvodstve 15(1), 20–25 (2017). (in Russian)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Yury Gagarin State Technical University of SaratovSaratovRussia
  2. 2.Institute of Precision Mechanics and Control of Russian Academy of SciencesSaratovRussia

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