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Bridging the gap between theory and practice in software QA

https://doi.org/10.54596/2958-0048-2024-4-213-225

Abstract

The article explores the gap between academic training in software testing and the realities of working in the industry. The results of hypothesis testing are presented in the form of a conversation. Using a dialogue between a student, a professor, and a senior QA specialist as an example, key challenges faced by graduates in transitioning from academic settings to real-world professional activities are discussed. The professor explains that the university’s software testing course is based on systematic principles, covering core testing methodologies and tools. Meanwhile, the experienced QA specialist provides practical examples, emphasizing the importance of adaptability in dynamic work settings, where project requirements often shift in terms of time, budget, and scope. The article focuses on how theory and practice in software testing can complement each other to achieve optimal results, even with limited resources.

About the Authors

V. P. Kulikova
Manash Kozybayev North Kazakhstan University NPLC
Kazakhstan

Kulikova Valentina P. - corresponding author, Professor, "Information and Communication Technologies" chair,  candidate of technical sciences, associate professor

Petropavlovsk



V. P. Kulikov
Manash Kozybayev North Kazakhstan University NPLC
Kazakhstan

Kulikov Vladimir P. - Professor, "Information and Communication Technologies" chair, candidate of physical  and mathematical sciences, associate professor, corresponding member of the international informatization academy

Petropavlovsk



E. V. Kulikova
Smart Solution, located Aurora
Canada

Kulikova Evgenia V. - Master Science in Computer Science; Solution Architect/Senior Business Analyst/BA  team manager

Ontario



References

1. Akhrameyko, A.A. (n.d.). On improving the efficiency o f m anagerial decision-m aking under conditions o f non-stochastic data uncertainty [Electronic resource]. Retrieved October 5, 2024, from http://bseu.by

2. Kulikova, VP. (2006). D ata analysis and processing in information systems: Educational and m ethodological manual. - Petropavlovsk: SKSU named after M. Kozybayev.

3. Mukhin, O.I. (n.d.). Lecture 36. Expertise [Electronic resource]. Retrieved October 5, 2024, from http://stratum.ac.rn

4. RSVPU. (n.d.). Using E xcel to summarize expert opinions using K em en y’s median m ethod [Electronic resource]. Retrieved October 5, 2024, from http://rsvpu.ru

5. MedStatistic. (n.d.). Online calculators fo r statistical indicators [Electronic resource]. Retrieved October 5, 2024, from http://medstatistic.ru

6. Kulikov, S.V. (2023). Software testing: Basic course (3rd ed.). Moscow.

7. Myers, G.J., Sandler, C., & Badgett, T. (2020). The art o f software testing (4th ed.). Wiley.

8. Aniche, M. (2022). Effective software testing. Manning Publications.

9. Applitools. (n.d.). Test Autom ation University [Electronic resource]. Retrieved October 5, 2024, from https://www.applitools.com

10. Ministry of Testing. (n.d.). Where software testers, QA, and quality engineers build their careers [Electronic resource]. Retrieved October 5, 2024, from https://www.ministryoftesting.com

11. StatSoft. (n.d.). B ig Data, data mining, predictive analytics, statistics. StatSoft Electronic Textbook [Electronic resource]. Retrieved October 5, 2024, from https://archive.org

12. Omsk Region. (n.d.). Probability theory - A dditional resources [Electronic resource]. Retrieved October 5, 2024, from http://newasp.omskreg.ru/probability/

13. Orlov, A.I. (2018). M ethods o f m anagerial decision-making: Textbook. Moscow: KNORUS.

14. Borodina, A.V., & Nekrasova, R.S. (2023). Statistical criteria in data analysis: Educational manual. Petrozavodsk: PetrSU Publishing.

15. Borovikov, V (2003). Statistica (Art o f data analysis on a computer). Moscow (St. Petersburg): Piter.

16. Habr. (n.d.). H ow to choose the right statistical test fo r different m etrics [Electronic resource]. Retrieved October 5, 2024.

17. Microsoft Support. Using the analysis toolpak [Electronic resource]. Retrieved November 5, 2024, from https://support.microsoft.com

18. Nikitin, O.R., & Korneeva, N.N. (2020). M ethods fo r m easuring statistical param eters o f radio signals: Textbook. Vladimir: Vladimir State University named after A.G. and N.G. Stoletovs.

19. BirdyX.ru. (n.d.). Non-param etric statistical criterion o f Kruskal-W allis [Electronic resource]. Retrieved November 12, 2024.

20. LibreTexts. (n.d.). K ruskal-W allis Test - Statistics [Electronic resource]. Retrieved November 12, 2024.

21. Crispin, L., & Gregory, J. (2019). A gile testing condensed: A b rie f introduction. AgileTester.

22. IBM. (n.d.). User guide fo r IB M SP SS Statistics 27 base system [Electronic resource]. Retrieved October 5, 2024.

23. SPSS Manual. (n.d.). SP SS instruction m anual [Electronic resource]. Retrieved October 5, 2024, from http://bsu.ac.ug

24. Real Python. (n.d.). Python statistics fundam entals: How to describe your data [Electronic resource]. Retrieved October 5, 2024, from https://realpython.com/python-statistics/

25. Python.org. (n.d.). Statistics-m athem atical statistics functions - Python 3.13.0 documentation [Electronic resource]. Retrieved October 5, 2024, from https://docs.python.org/3/library/statistics.html

26. Murray, A. (n.d.). A dvanced Excel success: A practical guide to m astering E xcel [Electronic resource]. Retrieved October 5, 2024, from https://doi.org/10.1007/978-1-4842-6467-6


Review

For citations:


Kulikova V.P., Kulikov V.P., Kulikova E.V. Bridging the gap between theory and practice in software QA. Vestnik of M. Kozybayev North Kazakhstan University. 2024;(4 (64)):213-225. https://doi.org/10.54596/2958-0048-2024-4-213-225

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ISSN 2958-003X (Print)
ISSN 2958-0048 (Online)