The Software Reliability Increase Method

Svetlana A. Yaremchuk, Dmitry A. Maevsky


Our investigation purpose is to create the software reliability increase method. The proposed method allows creators to calculate statistic, probabilistic and valuating reliability indices of software components which contain defects. The method’s aim is to take into consideration the statistic components complexity by means of composite metrics. The use of received indices provides for components finding which contain much more defects for refactoring and the first testing process. It contributes to increase identified and corrected defects quantity and improve the software reliability on average about 8%.


Software reliability; Complexity Software components; Defects; Predictable reliability indexes; Refactoring; Components testing

Full Text:



ISO/IEC 25010. (2011). Systems and Software engineering - Systems and software quality requirements and evaluations (SQuaRE) - System and Software Quality models.

Maevsky, D. A., & Yaremchuk, S. A. (2012). A priori estimation of the amount of faults in information system software. Radio Electronic and Computer Systems, 4(56), 73-80. Kharkiv: KHAI,.

Maevsky, D. A., & Yaremchuk, S. A. (2012). The estimation of the amount of software faults on the complexity metric basis. Electrical Engineering and Computer Systems, 07(83), 113–120. Kiev: Technica.

Neumann, P. G. (1995). Computer related risks. Reading. MA: Addison-Wesley.

IEEE Std 610.12. (1990). IEEE standard glossary of software engineering terminology.

Ma Y., Guo L., Cukic B. (2007). Statistical framework for the prediction of fault proneness. Advances in machine learning application in software engineering (pp.237–265). Idea Group Inc..

Mahaweerawat, A., Sophasathit, P., & Lursinsap, C. (2002). Software fault prediction using fuzzy clustering and radial basis function network. In International conference on intelligent technologies. Vietnam, 304-313.

Thwin, M. M. T., & Quah, T.-S. (2005). Application of neural networks for software quality prediction using object-oriented metrics. J. System Software. May., 76, 147–156.

Pomorova, O. V., & Hovorushchenko, T. O. (2012). The research of Mat Lab function features for scaling input data of Software quality evaluation artificial neural network. Radio Electronic and Computer Systems, 5(57). Kharkiv: KHAI, 219-224.

Fenton, N. E., & Neil, M. A. (1999). Critique of software defect prediction models. IEEE Trans. Softw. Eng., 25(5), 675–689.

The PROMISE Repository of empirical software engineering data. – 01-04-2014.



  • There are currently no refbacks.

Copyright (c)

Share us to:   


  • How to do online submission to another Journal?
  • If you have already registered in Journal A, then how can you submit another article to Journal B? It takes two steps to make it happen:

1. Register yourself in Journal B as an Author

  • Find the journal you want to submit to in CATEGORIES, click on “VIEW JOURNAL”, “Online Submissions”, “GO TO LOGIN” and “Edit My Profile”. Check “Author” on the “Edit Profile” page, then “Save”.

2. Submission

  • Go to “User Home”, and click on “Author” under the name of Journal B. You may start a New Submission by clicking on “CLICK HERE”.

We only use three mailboxes as follows to deal with issues about paper acceptance, payment and submission of electronic versions of our journals to databases:;;

 Articles published in Studies in Sociology of Science are licensed under Creative Commons Attribution 4.0 (CC-BY).


Address: 9375 Rue de Roissy Brossard, Québec, J4X 3A1, Canada

Telephone: 1-514-558 6138
Website: Http://; Http://

Copyright © 2010 Canadian Research & Development Centre of Sciences and Cultures