Global Research Trends in Quality Assurance, Software Testing, and Software Quality: A Bibliometric Analysis
Keywords:
Software quality assurance, software testing, defect prediction, large language models, bibliometric analysis, DevOpsAbstract
This study conducts a bibliometric analysis of research trends in Quality Assurance, Software Testing, Software Quality, and Quality Engineering to examine the development, intellectual structure, influential contributions, and emerging thematic directions within the field. Bibliographic data retrieved from the Scopus database were analyzed using VOSviewer to provide a systematic representation of the research landscape across multiple publication years. Three major bibliometric techniques were employed: citation analysis, co-citation analysis, and co-word analysis. Citation analysis was used to identify influential documents, publication sources, authors, and countries, while co-citation analysis examined relationships among frequently cited references to reveal the intellectual foundations and major research clusters of Software Quality Assurance. Co-word analysis was applied to keyword relationships to identify prominent and emerging research themes within the literature. The findings demonstrate that contemporary Software Quality Assurance research is supported by several interconnected areas, including software testing, test automation, software defect prediction, software metrics, model validation, machine learning, code review, DevOps, and intelligent quality engineering. The co-citation analysis further reveals that traditional software testing and measurement principles remain important intellectual foundations, while machine learning-based defect prediction, automated testing, and intelligent software analysis represent increasingly significant research directions. Overall, the findings indicate a continuing transition from conventional testing and quality control toward more automated, continuous, predictive, data-driven, and AI-assisted Quality Engineering approaches. The study provides a structured understanding of the evolving Software Quality Assurance research landscape and highlights the growing integration of established software engineering practices with emerging intelligent technologies.
References
Alshamaa, Z. M., & Saleem, N. N. (2025). Black box software testing techniques: A literature review. Passer Journal of Basic and Applied Sciences, 7(2), 1001–1011. https://doi.org/10.24271/psr.2025.508216.1963
Antal, G., Bán, D., Isztin, M., Ferenc, R., & Hegedűs, P. (2025). Leveraging GPT-4 for vulnerability-witnessing unit test generation. Proceedings of the 29th International Conference on Evaluation and Assessment in Software Engineering (EASE 2025), 1056–1065. https://doi.org/10.1145/3756681.3757075
Antonio, A. A. F., Alfredo, B. P., & Gustavo, S. G. R. (2025). Engineering prompt-orchestrated LLM workflows for automated test case generation in agile environments. International Journal of Advanced Computer Science and Applications, 16(12), 771–781. https://doi.org/10.14569/IJACSA.2025.0161272
Bhagwat, A. (2009, December 28). Software test case engineering: Treating test cases as a product. StickyMinds.
Bogorin-Predescu, A., Titu, S., Titu, A. M., Marcu, D. F., Dragomir, D. C., & Dragomir, M. (2025). Innovative cost-effective embedded system to enhance ECU firmware quality through remote hybrid testing in automotive domain. Applied Sciences (Switzerland), 15(23), Article 12736. https://doi.org/10.3390/app152312736
Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Lim, W. M. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research, 133, 285–296.
Eagal, A., Stolee, K. T., & Ore, J.-P. (2025). Analyzing the dependability of large language models for code clone generation. Journal of Systems and Software, 230, Article 112548. https://doi.org/10.1016/j.jss.2025.112548
Esposito, M., Robredo, M., Arcelli Fontana, F., & Lenarduzzi, V. (2025). On the correlation between architectural smells and static analysis warnings. Software Quality Journal, 33(4), Article 33. https://doi.org/10.1007/s11219-025-09730-7
Hai, T., Zhou, J., Li, N., Jain, S. K., Agrawal, S., & Ben Dhaou, I. (2022). Cloud-based bug tracking software defects analysis using deep learning. Journal of Cloud Computing, 11, 32. https://doi.org/10.1186/s13677-022-00311-8
López, L., Manzano, M., Gómez, C., Oriol, M., Farré, C., Franch, X., Martínez-Fernández, S., & Vollmer, A. M. (2021). QaSD: A quality-aware strategic dashboard for supporting decision makers in agile software development. Science of Computer Programming.
Olcay, H. H., Belen, E., & Hacıoğlu, E. (2025). The review of test processes on corporate quality assurance (QA) at SeturTech. Proceedings of the 29th International Conference on Evaluation and Assessment in Software Engineering (EASE 2025), 826–829. https://doi.org/10.1145/3756681.3757008
Parija, S., Muduli, D., Shameem, M., & Sharma, S. K. (2025). Enhancing software defect prediction: A stacking ensemble framework with advanced feature engineering. Proceedings of the 29th International Conference on Evaluation and Assessment in Software Engineering Companion (EASE Companion 2025), 28–34. https://doi.org/10.1145/3727967.3756848
Parveen, T., Tilley, S., & Gonzalez, G. (2007). A case study in test management. Proceedings of the 45th Annual ACM Southeast Conference (ACM-SE 45), 82–87.
Rafi, S., Akbar, M. A., & Khan, A. A. (2025). Assessing software product quality in DevOps: An ISO 25010:2023 perspective. Proceedings of the 29th International Conference on Evaluation and Assessment in Software Engineering Companion (EASE Companion 2025), 10–16. https://doi.org/10.1145/3727967.3756847
Sultan, T., Khedr, A. E., & Sayed, M. (2013). A proposed defect tracking model for classifying the inserted defect reports to enhance software quality control. International Journal of Computer Applications, 67(14).
Vollmer, A. M., Martínez-Fernández, S., Bagnato, A., Partanen, J., López, L., & Rodríguez, P. (2019). Practical experiences and value of applying software analytics to manage quality. Proceedings of ESEM 2019. arXiv:1910.11007.
Yang, G. (2025). CTQRS-based reinforcement learning framework for reliable bug report generation using open-source large language models. Applied Sciences (Switzerland), 15(23), Article 12545. https://doi.org/10.3390/app152312545
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Journal for the Advancement of Sustainable Development Goals
This work is licensed under a Creative Commons Attribution 4.0 International License.
License Statement
Creative Commons Attribution 4.0 International License (CC BY 4.0)
All articles published in the Journal for the Advancement of Sustainable Development Goals (JASDG) are licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
Under this license, users are free to:
-
Share — copy and redistribute the material in any medium or format;
-
Adapt — remix, transform, and build upon the material for any purpose, including commercial use;
provided that appropriate credit is given to the original author(s) and the source, a link to the license is provided, and any changes made are indicated.
The licensor cannot revoke these freedoms as long as the license terms are followed.
Authors retain copyright of their work while granting JASDG the right of first publication. The published work remains freely available to readers and may be used, distributed, reproduced, and adapted by others in accordance with the terms of the CC BY 4.0 license.
For more information about this license, please visit:
https://creativecommons.org/licenses/by/4.0/
Copyright Notice
Copyright © 2026 The Author(s).
Articles published in the Journal for the Advancement of Sustainable Development Goals (JASDG) are distributed under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, reproduction, and adaptation in any medium, provided the original author(s) and source are properly credited.
