Revisiting the Foundations of Quality Assurance: Reflections on Most-Cited Research and Their Real-World Impact

Abstract

Quality Assurance (QA) continues to serve as the backbone of modern software engineering despite the wave of automation and AI-driven development. In this perspectives article, I reflect on some of the most cited QA research papers from the last decade, drawing from their findings and comparing them to my hands-on experience in large-scale financial infrastructures. This paper highlights the gaps between academic proposals and industry needs, underlines the timeless relevance of human-led QA strategies, and suggests how the next generation of QA research can be made more actionable, ethical, and aligned with agile realities.

Introduction

The article reflects on the gap between academic QA research and real-world quality assurance challenges, especially in high-stakes industries like finance. While recent QA research has advanced areas such as automated testing, AI-driven test generation, and continuous testing, these approaches often assume ideal conditions that rarely exist in complex enterprise environments like Visa’s.

From the perspective of a senior QA engineer, practical challenges include messy legacy data hindering AI testing, continuous testing focusing too much on speed rather than quality, and test prioritization overlooking business-critical risk factors. Some academic innovations, like AI-based flaky test detection and static code analysis, have been adapted successfully in practice.

The author argues that future QA research should address human factors such as burnout, focus on automating hard-to-detect edge cases, clarify accountability when AI testing fails, and better align QA efforts with actual business impact.

Conclusion

QA engineers are not just bug-hunters—we’re gatekeepers, ethical reviewers, and user advocates. The most cited QA research has moved the field forward, but it’s time for convergence. Academic research must align more with the unpredictable, nuanced, and human side of real-world QA.

References

[1] Ammann, P., & Offutt, J. (2019). Introduction to Software Testing. Cambridge University Press. [2] Anand, S., Burke, E. K., Chen, T. Y., Clark, J., Harman, M., Hierons, R. M., ... & Yoo, S. (2013). An orchestrated survey of methodologies for automated software test case generation. Journal of Systems and Software, 86(8), 1978-2001. [3] Li, Z., Harman, M., & Hierons, R. M. (2007). Search algorithms for regression test case prioritization. IEEE Transactions on Software Engineering, 33(4), 225-237. [4] Chen, T. Y., Kuo, F. C., Merkel, R. G., & Tse, T. H. (2010). Adaptive random testing: The ART of test case diversity. Journal of Systems and Software, 83(1), 60-66. [5] Garousi, V., Felderer, M., & Mäntylä, M. V. (2019). The need for more industry–academia collaborations in software testing: Opportunities and challenges. Information and Software Technology, 98, 20-38.

Copyright

Copyright © 2025 Shivani Choudhary. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.