Benefits of an Application of Evolutionary Strategy in the Process of Test Data Generation

[1] S.G. Ahmed. Automatic generation of basis test paths using variable length genetic algorithm, Journal Information Processing Letters, Volume 114 Issue 6, June, 2014, pp. 304-316.10.1016/j.ipl.2014.01.009Search in Google Scholar

[2] R. Alavi, S. Lofti. The New Approach for Software Testing Using a Genetic Algorithm Based on Clustering Initial Test Instances, International Conference on Computer and Software Modeling 2011, IPCSIT vol.14 (2011).Search in Google Scholar

[3] E. Alba, F. Chicano. Observations in using parallel and sequential evolutionary algorithms for automatic software testing, Computers & Operations Research, Vol. 35 Issue 10, October 2008, pp. 3163-3183.10.1016/j.cor.2007.01.016Search in Google Scholar

[4] A. Aleti, L. Grunske. Test data generation with a Kalman filter-based adaptive genetic algorithm, The Journal of Systems and Software Vol. 103, May 2015 pp. 343-352.10.1016/j.jss.2014.11.035Search in Google Scholar

[5] M. Alshraideh, B.A. Mahafzah, S. Al-Sharaeh. A multiple- population genetic algorithm for branch coverage test data generation, Software Quality Journal, Vol. 19, Volume 19, Issue 3 September 2011, pp. 489-513.10.1007/s11219-010-9117-4Search in Google Scholar

[6] D. Farley, J. Humble. Ciągłe dostarczanie oprogramowania, Helion 2015.Search in Google Scholar

[7] D. Gong, T. Tian, X. Yao. Grouping target paths for evolutionary generation of test data in paralel, The Journal of Systems and Software, Vol. 85, Issue 11, November 2012, pp. 2531-2540.10.1016/j.jss.2012.05.071Search in Google Scholar

[8] D. Gong, Y. Zhang. Generating test data for both path coverage and fault detection using genetic algorithms, Frontiers of Computer Science, December 2013, Vol. 7, Issue 6, pp. 822-837.10.1007/s11704-013-3024-3Search in Google Scholar

[9] D. Gong, Y. Zhang. Generating test data for both path coverage and fault detection using genetic algorithms: multi-path case, Frontiers of Computer Science, October 2014, Vol. 8, Issue 5, pp. 726-740.10.1007/s11704-014-3372-7Search in Google Scholar

[10] M.J. Harrold, R. Pargas, R. R. Peck. Test-Data generation using genetics algorithms, Journal of Software Testing, Verification and Reliability 1999.Search in Google Scholar

[11] I. Hermadi, C. Lokan, R. Sarker. Dynamic stopping criteria for search-based test data generation for path testing , Information and Software Technology, April 2014 Vol. 56, pp. 395-407.10.1016/j.infsof.2014.01.001Search in Google Scholar

[12] J. Hudec, E. Gramatova. An Efficient functional test generation method for processors using genetic algorithms, Journal of Electrical Engineering, July 2015, Vol. 66, Issue. 4, pp. 186-193.10.2478/jee-2015-0031Search in Google Scholar

[13] N. Khurana, R.S. Chillar. Test Case Generation and Optimization using UML Models and Genetic Algorithm, Procedia Computer Science, August 2015, Vol. 57, pp. 966-1004.10.1016/j.procs.2015.07.502Search in Google Scholar

[14] H. Kim, P.R. Srivastava. Application of Genetic Algorithm in Software Testing, International Journal of Software Engineering and Its Applications, October 2009, Vol. 3, Issue 4, pp. 87-96.Search in Google Scholar

[15] R. Krishnamoorthi, A. Sahaaya, S.A. Mary. Regression Test Suite Prioritization using Genetic Algorithms, International Journal of Hybrid Information Technology, July 2009 Vol.2, Issue .3, July.Search in Google Scholar

[16] C. Mao. Harmony search-based test data generation for branch coverage in software structural testing, Neural Computing and Applications, September 2013, Vol. 25, Issue 1, pp.199-216. Springer-Verlag London 2013.10.1007/s00521-013-1474-zSearch in Google Scholar

[17] M. Mirzaaghaei, F. Pastore, M. Pezze. Automatic test case evolution, Software testing, Verification and Reliability, April 2014, Vol. 24, Issue 5, pp.386-411.10.1002/stvr.1527Search in Google Scholar

[18] A. Piaskowy, R. Smilgin. Dane Testowe, teoria i praktyka, Helion 2011.Search in Google Scholar

[19] A. Roman, Testowanie i jakość oprogramowania, PWN 2015.Search in Google Scholar

[20] D. Rutkowska, M. Piliński, L. Rutkowski. Sieci neuronowe, algorytmy genetyczne i systemy rozmyte, PWN Wwa 1997.Search in Google Scholar

[21] D. Warchoł, M. Żukowicz. Testing education: test case prioritization using matrices, General and Professional Education, May 2015, Vol. 1, Issue 8, pp. 57-62.Search in Google Scholar

[22] M. Żukowicz. Edukacja testowania: Narzędzie All-pairs Testing w procesie optymalizacji testów konfiguracji - zastosowanie narzędzia w systemie B2B OPTIbud, General and Professional Education, Dezember 2015, Vol. 4, Issue 12, pp. 99-106.Search in Google Scholar

[23] M. Żukowicz. Edukacyjne i ekonomiczne aspekty zastosowania cyklu Hamiltona w projektowaniu i testowaniu oprogramowania, General and Professional Education, numer Dezember 2014, Vol. 4, Issue 13, pp. 95-102.Search in Google Scholar

[24] M. Żukowicz, O pewnych problemach analizy wartości brzegowych, Internet:, http://testerzy.pl/materialy/index.php?file=analiza-wartosci-brzegowych.pdf, access date 03.03.2016.Search in Google Scholar