Research of intelligent methods of software testing




software testing, automated testing, artificial intelligence


This article presents the examination of several techniques and tools used in the automated software testing process. Considering the ever-growing importance of software testing, several possible implications of implementation of artificial intelligence into this area are also discussed. The main objective of this study is to examine the field of test automation by categorising related test activities, to which artificial intelligence tools can be applied for increased efficiency, and evaluate the impact of the application. The main software testing methods are white-box, black-box, and grey-box methods; an effort has been made to determine a connection between the given testing methods and artificial intelligence methods. A brief summary of several artificial intelligence engine tools used to automate testing was also provided. Lastly, the possible future benefits from usage of AI in software testing was investigated.

Author Biographies

Tofig Kazimov, Institute of Information Technology of Azerbaijan National Academy of Sciences, Baku

Tofig H. Kazimov,

associate professor, Doctor of Physical and Mathematical Sciences, the head of the Department of the Institute of Information Technology of Azerbaijan National Academy of Sciences, Baku, Azerbaijan.

Tamilla Bayramova, Institute of Information Technology of Azerbaijan National Academy of Sciences, Baku

Tamilla A. Bayramova,

the chief specialist of the Institute of Information Technology of Azerbaijan National Academy of Sciences, Baku, Azerbaijan.

Nazakat Malikova, Institute of Information Technology of Azerbaijan National Academy of Sciences, Baku

Nazakat J. Malikova,

a senior researcher at the Institute of Information Technology of Azerbaijan National Academy of Sciences, Baku, Azerbaijan.


A. Dennis, B.H. Wixom, and D. Tegarden, Systems Analysis and Design with OOP Approach with UML 2.0; 4th edition. USA: John Wiley & Sons, Inc., 2009, 691 p.

G.J. Myers, C. Sandler, and T. Badgett, The Art of Software Testing; 3rd edition. Canada: John Wiley & Sons, Inc., 2012, 420 p.

A. Dennis, B.H. Wixom, and R.M. Roth., Systems Analysis and Design; 5th edition. USA: John Wiley & Sons, Inc., 2012, 594 p.

D.R. Graham, “Testing, Verification and Validation”, Int. J., vol. XVI, pp. 1069–1101, 1979.

D. Huizinga and A. Kolawa, Automated defect prevention. Hoboken, N.J.: Wiley-Interscience, 2007, 454 p.

M. Polo, P. Reales, M. Piattini, and C. Ebert, “Test automation”, IEEE Software, vol. 30, no. 1, pp. 84–89, 2013.

M.A. Umar, “Comprehensive study of software testing: Categories, levels, techniques and types”, International Journal of Advance Research, Ideas and Innovations in Technology, vol. 5, no. 6, pp. 32–40, 2019.

U. Mubarak Albarka, and C. Zhanfang, “A Study of Automated Software Testing: Automation Tools and Frameworks”, International Journal of Computer Science Engineering (IJCSE), vol. 8, pp. 217–225, 2019.

L. Luo, A Report on Software Testing Techniques. Pittsburgh, USA, 2003, 20 p.

O. Carol, “Why We Need New Software Testing Technologies”, Conference At-A-Glance PNSQC World Trade Center 121 SW Salmon St. Portland, pp. 226–248, 2019.

I. Jovanovic, “Software Testing Methods and Techniques”, IPSI BgD Trans. Internet Res., vol. 5, no. 1, pp. 30–41, 2009.

E. Khan, “Different Forms of Software Testing Techniques for Finding Errors”, Int. J. Comput. Sci., vol. 7, no. 3, pp. 11–16, 2010.

M.E. Khan and F. Khan, “A comparative study of white box, black box and grey box testing techniques”, International Journal of Advanced Computer Science and Applications, vol. 3, no. 6, pp. 12–15, 2012.

R.S. Pressman, Software Engineering: A Practitioner’s Approach; 6th edition. Chapter 14: Software Testing Techniques, & Associates, Inc., 2005, 402 p.

F. Redmill, “Theory and Practice of Risk-based Testing”, Software testing, vol. 15, no. 1, pp. 3–20, 2005.

R. Lima, A.M. Cruz, and J. Ribeiro, “Artificial Intelligence Applied to Software Testing: A Literature Review”, 15th Iberian Conference on Information Systems and Technologies (CISTI), pp. 6–7, 2020. Available: 2020. 9141124.

J. Wang and C. Zhang, “Software reliability prediction using a deep learning model based on the RNN encoder–decoder”, Reliability Engineering & System Safety, vol. 170, pp. 73–82, 2018.

H.L.P. Raj and K. Chandrasekaran, “NEAT Algorithm for Testsuite generation in Automated Software Testing”, IEEE Symposium Series on Computational Intelligence (SSCI), pp. 2361–2368, 2018.

A.D. Danilov and V.M. Mugatin, “Solving the optimization problem for regression testing using a neural network approach”, Modeling, optimization and information technology, 8 (1), pp. 1–8, 2020.

I.S. Polevshikov and R.A. Faizrakhmanov, “Automated control of testing software systems using neural networks”, Engineering Bulletin of Don, no. 4 (51), pp. 94–105, 2018.

M. Hossain, S. Abufardeh, and S. Kumar, “Frameworks for Performing on Cloud Automated Software Testing Using Swarm Intelligence Algorithm: Brief Survey”, Advances in Science, Technology and Engineering Systems Journal, vol. 3, no. 2, pp. 252–256, 2018.

H. Li and P.L. Chiou, “Software Test Data Generation using Ant Colony Optimization”, International conference on computational intelligence, pp. 1–4, 2004.

A. Tripathi, Sh. Srivastava, H. Mittal, Sh. Sinha, and V. Yadav, “Multi-Objective ANT Lion Optimization Algorithm Based Mutant Test Case Selection for Regression Testing”, Journal of Scientific & Industrial Research, vol. 80, pp. 582–592, 2021.

K. Karnavel and J. Santhoshkumar, “Automated Software Testing for Application Maintenance by using Bee Colony Optimization algorithms (BCO)”, Information Communication and Embedded Systems (ICICES), 2013 International Conference on IEEE, pp. 327–330, 2013.

I. Zarembo, “Analysis of artificial intelligence applications for automated testing of video games”, Environment Technologies Resources Proceedings of the International Scientific and Practical Conference, pp.170–174, 2019.

M. Mikael, “Utilizing Artificial Intelligence in Software Testing”, Metrolopolia University of Applied Science, pp. 46–47, 2019. Available:

Eggplant Software. Available:

A. Jones, Artificial Intelligence Tools for Software Testing. Available:

R. Subramanian, “How AI is transforming software testing”, Selenium Conference, Chicago, 2018. Available:

5 Popular AI-powered tools for test automation. Available:

H. Hourani, A. Hammad, and M. Lafi, “The Impact of Artificial Intelligence on Software Testing”, IEEE Jordan International Joint Conference on Electrical Engineering and Information Technology (JEEIT), pp. 568–569, 2019. Available:






Progressive information technologies, high-efficiency computer systems