Impact of Generative Artificial Intelligence on Knowledge Management in Software Engineering: A Systematic Mapping Study
Resumo
Context: In recent years, Generative Artificial Intelligence (GenAI) has emerged as a transformative technology with high potential across various organizational contexts. Leveraging techniques such as Natural Language Processing (NLP) and Large Language Models (LLMs), tools like ChatGPT, GitHub Copilot, and DALL-E have begun to facilitate information creation and retrieval, automate tasks, and optimize decision-making processes. In the field of Knowledge Management (KM), GenAI has served as an enabler for organizing, disseminating, and reusing knowledge, fostering more collaborative and responsive environments. Specifically in Software Engineering, GenAI demonstrates potential to enhance productivity. The integration of GenAI with KM practices can play a key role in improving software quality, by enabling better reuse of knowledge, supporting consistent practices, and promoting informed decision-making. Understanding how GenAI can support KM in this domain is therefore essential to guide its strategic and effective integration. Objective: This study aims to investigate the impact of GenAI on KM processes within organizations operating in the Software Engineering domain. Method: To achieve this, we conducted a systematic mapping study to identify current practices, perceptions, and challenges related to the use of GenAI in KM within Software Engineering. Results: The study analyzed 97 primary studies published between 2021 and 2025. Main results indicate that GenAI focusing on code generation and software construction activities. KM practices most commonly supported include knowledge application and creation, with externalization emerging as the dominant SECI knowledge conversion mode. Conclusion: GenAI contributes significantly to KM in Software Engineering by supporting operational tasks and the formalization of knowledge. However, limited attention to knowledge dissemination and deeper learning processes reveals promising directions for future research.
Palavras-chave:
Software Engineering, Knowledge Management, Generative Artificial Intelligence, Systematic Mapping Study
Referências
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John Aldrich. Generative ai and organizational knowledge: A review of current trends and future directions. Journal of Knowledge Management, 27(8):1756–1772, 2023.
Ikujiro Nonaka and Hirotaka Takeuchi. The Knowledge-Creating Company: How Japanese Companies Create the Dynamics of Innovation. Oxford University Press, 1995.
Christoph Ziegler, Ute Schmid, and Martin Kramer. Productivity evaluation of github copilot as a pair-programmer in an introductory programming course. arXiv preprint arXiv:2210.14135, 2022.
Prithvi Vaithilingam, Alaa Dakhel, and Michael Pradel. Expectation vs. experience: Evaluating the usability of code generation tools powered by large language models. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22), pages 1–13. ACM, 2022.
Daniel Russo. Navigating the complexity of generative ai adoption in software engineering. ACM Transactions on Software Engineering and Methodology, 33(5):1– 50, 2024.
David Sobania, Simon Krieghoff, Dennis Trautsch, Jens Grabowski, and Lennart Wermke. An empirical study on the usage of github copilot. In Proceedings of the 45th International Conference on Software Engineering (ICSE). IEEE/ACM, 2023.
Rémi Perrier, Xi Zhang, and Li Chen. Ai and software engineering: Ethical and practical considerations. Software Engineering Notes, 48(2):1–6, 2023.
Xiaoyuan Lyu, Xiaohan Wang, Ziyang Li, Hongyu Yang, Hongyu Zhang, and David Lo. Gpt for se: Opportunities and pitfalls in adopting generative language models in software engineering. arXiv preprint arXiv:2305.04639, 2023.
Yuxuan Zhang, Siqi Sun, Philip S. Yu, and et al. A survey of large language models. arXiv preprint arXiv:2303.18223, 2023.
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Sheikh Abdulaziz Fahad, Said A Salloum, and Khaled Shaalan. The role of chatgpt in knowledge sharing and collaboration within digital workplaces: a systematic review. Artificial intelligence in education: The power and dangers of ChatGPT in the classroom, pages 259–282, 2024.
Tajinder Kumar, Vishal Garg, Sachin Lalar, and Rajinder Kumar. Measuring impact of generative ai in software development and innovation. In International Conference on Entrepreneurship, Innovation, and Leadership, pages 57–67. Springer, 2023.
Juliana dos Santos, Guilherme Augusto Martins, Érica de Souza, Katia Felizardo, and Giovani Meinerz. Perceptions of knowledge management in brazilian software development companies. In XXV Congresso Ibero-Americano em Engenharia de Software, pages 233–247, 2022.
P. Wendorff and D. Apshvalka. The knowledge management strategy of agile software development. pages 607–614, Univ. of Limerick, Ireland, 1998. 6th European Conference on Knowledge Management.
G. A. Ruiz, P. R. da Silva, E. F. de Souza, K. R. Felizardo, G. V. Meinerz, and N. L. Vijaykumar. Knowledge management in agile testing teams: a survey. In 21𝑠𝑡 Ibero-American Conference on Software Engineering (CIbSE’ 18), pages 1–8, 2018.
Victor Cabrero-Daniel et al. Ai for agile development: a meta-analysis. arXiv preprint arXiv:2305.08093, 2023.
Héctor Cornide-Reyes, Diego Monsalves, Esteban Durán, and Jorge Silva. Generative artificial intelligence in agile software development processes: A literature review focused on user experience. In Lecture Notes in Computer Science, volume 15787, pages 242–259. Springer, 2025.
Kai Petersen, Robert Feldt, Shahid Mujtaba, and Michael Mattsson. Systematic mapping studies in software engineering. In International Conference on Evaluation and Assessment in Software Engineering (EASE), pages 1–10, 2008.
B.A. Kitchenham and S. Charters. Guidelines for performing systematic literature reviews in software engineering. Technical report, Keele University and Durham University Joint Report, 2007. Technical Report EBSE 2007–001.
D. Maplesden, E. Tempero, J. Hosking, and J.G. Grundy. Performance analysis for object-oriented software: A systematic mapping. IEEE Transaction on Software Engineering, 41(7):691–710, 2015.
H. Zhang, B.A. Muhammad, and T. Paolo. Identifying relevant studies in software engineering. Information and Software Technology, 53(6):625–637, 2011.
Lili Bo, Yuting He, Xiaobing Sun, Wangjie Ji, and Xiaohan Wu. A software bug fixing approach based on knowledge-enhanced large language models. In 2024 IEEE 24th International Conference on Software Quality, Reliability and Security (QRS), pages 169–179. IEEE, 2024.
Nathan Hagel, Nicolas Hili, and Didier Schwab. Turning low-code development platforms into true no-code with llms. In Proceedings of the ACM/IEEE 27th International Conference on Model Driven Engineering Languages and Systems, pages 876–885, 2024.
Arianna Johnson. Here’s what to know about openai’s ChatGPT — what it’s disrupting and how to use it. Forbes. Acessado em 18 de julho de 2025.
Matthew B. Miles, A. Michael Huberman, and Johnny Saldaña. Qualitative Data Analysis: A Methods Sourcebook. SAGE Publications, Thousand Oaks, CA, 3rd edition, 2014.
Suresh Babu Nettur, Shanthi Karpurapu, Unnati Nettur, and Likhit Sagar Gajja. Cypress copilot: Development of an ai assistant for boosting productivity and transforming web application testing. IEEE Access, 2024.
IEEE Computer Society. Guide to the Software Engineering Body of Knowledge (SWEBOK Guide) – Version 4.0. IEEE, 2024. Accessed: July 2025.
Mafura Uandykova, Laura Baitenova, Gulnar Mukhamejanova, Assel Yeleukulova, and Tolkyn Mirkassimova. Java coding using artificial intelligence. Frontiers in Computer Science, Volume 6 - 2024, 2024.
Rebeka Tóth, Tamas Bisztray, and László Erdődi. Llms in web development: Evaluating llm-generated php code unveiling vulnerabilities and limitations. In International Conference on Computer Safety, Reliability, and Security, pages 425–437. Springer, 2024.
Nenad Petrović, Samir Koničanin, and Suad Suljović. Chatgpt in iot systems: Arduino case studies. In 2023 IEEE 33rd International Conference on Microelectronics (MIEL), pages 1–4. IEEE, 2023.
Saki Imai. Is github copilot a substitute for human pair-programming? an empirical study. In Proceedings of the ACM/IEEE 44th International Conference on Software Engineering: Companion Proceedings, pages 319–321, 2022.
Christian Bird, Denae Ford, Thomas Zimmermann, Nicole Forsgren, Eirini Kalliamvakou, Travis Lowdermilk, and Idan Gazit. Taking flight with copilot. Communications of the ACM, 66(6):56–62, 2023.
Kimiz Dalkir. Knowledge management in theory and practice. routledge, 2013.
Anh Nguyen-Duc and Dron Khanna. Value-based adoption of chatgpt in agile software development: A survey study of nordic software experts. In Generative AI for Effective Software Development, pages 257–273. Springer, 2024.
Antonio Della Porta, Vincenzo De Martino, Gilberto Recupito, Carmine Iemmino, Gemma Catolino, Dario Di Nucci, Fabio Palomba, et al. Using large language models to support software engineering documentation in waterfall life cycles: Are we there yet? In CEUR WORKSHOP PROCEEDINGS, volume 3762, pages 452–457. CEUR-WS, 2024.
Michael J Muller, April Yi Wang, Steven I Ross, Justin D Weisz, Mayank Agarwal, Kartik Talamadupula, Stephanie Houde, Fernando Martinez, John T Richards, Jaimie Drozdal, et al. How data scientists improve generated code documentation in jupyter notebooks. In IUI Workshops, 2021.
Lahbib Naimi, Abdeslam Jakimi, Rachid Saadane, Abdellah Chehri, et al. Automating software documentation: Employing llms for precise use case description. Procedia Computer Science, 246:1346–1354, 2024.
Liuchun Zhan and Changjiang Huang. Research on computer intelligent chatgpt natural language processing system based on scientific knowledge graph. In Proceedings of the 2024 International Conference on Machine Intelligence and Digital Applications, pages 47–51, 2024.
Daksh Chaudhary, Sri Lakshmi Vadlamani, Dimple Thomas, Shiva Nejati, and Mehrdad Sabetzadeh. Developing a llama-based chatbot for ci/cd question answering: A case study at ericsson. In 2024 IEEE International Conference on Software Maintenance and Evolution (ICSME), pages 707–718. IEEE, 2024.
Ningzhi Tang, Meng Chen, Zheng Ning, Aakash Bansal, Yu Huang, Collin McMillan, and Toby Jia-Jun Li. Developer behaviors in validating and repairing llmgenerated code using ide and eye tracking. In 2024 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC), pages 40–46. IEEE, 2024.
Soham Patel, Kailas Patil, and Prawit Chumchu. Bhramari: Bug driven highly reusable automated model for automated test bed generation and integration. Software Impacts, 21:100687, 2024.
Yuxin Qiu, Jie Hu, Qian Zhang, and Heng Yin. Calico: Automated knowledge calibration and diagnosis for elevating ai mastery in code tasks. In Proceedings of the 33rd ACM SIGSOFT International Symposium on Software Testing and Analysis, pages 1785–1797, 2024.
Xin Zhou, Yuqin Jin, He Zhang, Shanshan Li, and Xin Huang. A map of threats to validity of systematic literature reviews in software engineering. In 23rd Asia-Pacific Software Engineering Conferencees, pages 153–160, 2016.
Bianca M. Napoleão, Katia R. Felizardo, Érica F. de Souza, Fabio Petrillo, Sylvain Hallé, Nandamudi L. Vijaykumar, and Elisa Y. Nakagawa. Establishing a search string to detect secondary studies in software engineering. In 47th Euromicro Conference on Software Engineering and Advanced Applications (SEAA), pages 9–16, 2021.
Rishi Bommasani, Barret Zoph, et al. On the opportunities and risks of foundation models. arXiv preprint arXiv:2108.07258, 2021.
Erik Brynjolfsson, Danielle Li, and Lindsey Raymond. Generative ai at work. The Quarterly Journal of Economics, page qjae044, 2025.
Grant Cooper. Examining science education in chatgpt: An exploratory study of generative artificial intelligence. Journal of science education and technology, 32(3):444–452, 2023.
Viriya Taecharungroj. “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing, 7(1):35, 2023.
John Aldrich. Generative ai and organizational knowledge: A review of current trends and future directions. Journal of Knowledge Management, 27(8):1756–1772, 2023.
Ikujiro Nonaka and Hirotaka Takeuchi. The Knowledge-Creating Company: How Japanese Companies Create the Dynamics of Innovation. Oxford University Press, 1995.
Christoph Ziegler, Ute Schmid, and Martin Kramer. Productivity evaluation of github copilot as a pair-programmer in an introductory programming course. arXiv preprint arXiv:2210.14135, 2022.
Prithvi Vaithilingam, Alaa Dakhel, and Michael Pradel. Expectation vs. experience: Evaluating the usability of code generation tools powered by large language models. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22), pages 1–13. ACM, 2022.
Daniel Russo. Navigating the complexity of generative ai adoption in software engineering. ACM Transactions on Software Engineering and Methodology, 33(5):1– 50, 2024.
David Sobania, Simon Krieghoff, Dennis Trautsch, Jens Grabowski, and Lennart Wermke. An empirical study on the usage of github copilot. In Proceedings of the 45th International Conference on Software Engineering (ICSE). IEEE/ACM, 2023.
Rémi Perrier, Xi Zhang, and Li Chen. Ai and software engineering: Ethical and practical considerations. Software Engineering Notes, 48(2):1–6, 2023.
Xiaoyuan Lyu, Xiaohan Wang, Ziyang Li, Hongyu Yang, Hongyu Zhang, and David Lo. Gpt for se: Opportunities and pitfalls in adopting generative language models in software engineering. arXiv preprint arXiv:2305.04639, 2023.
Yuxuan Zhang, Siqi Sun, Philip S. Yu, and et al. A survey of large language models. arXiv preprint arXiv:2303.18223, 2023.
Tom B. Brown, Benjamin Mann, Nick Ryder, and et al. Language models are fewshot learners. Advances in Neural Information Processing Systems, 33:1877–1901, 2020.
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Generative adversarial nets. Advances in Neural Information Processing Systems, 27, 2014.
Antonia Creswell, Tom White, Vincent Dumoulin, Kai Arulkumaran, Bhavani Sengupta, and Anil A. Bharath. Generative adversarial networks: An overview. IEEE Signal Processing Magazine, 35(1):53–65, 2018.
D.E. O’Leary and R. Studer. Knowledge management: an interdisciplinary approach. IEEE Intelligent Systems, 16, No. 1, 2001.
K. Dalkir. Knowledge Management in Theory and Practice. Elsevier, USA, 3 edition, 2017.
K. M. Wiig. Knowledge Management Foundations. Schema Press, Limited, USA, 1 edition, 1994.
M. H. Meyer and M. H. Zack. The design and development of information products. MIT Sloan Management Review, 37:43, 1996.
M. McElroy. The knowledge life cycle. In ICM Conference on KM, Miami, USA, 1999. ICM.
Wendi R Bukowitz and Ruth L Williams. The knowledge management fieldbook. Financial Times/Prentice Hall, Hoboken, EUA, 1 edition, 2000.
Bianca Minetto Napoleão, Érica Ferreira de Souza, Glauco Antonio Ruiz, Katia Romero Felizardo, Giovani Volnei Meinerz, and Nandamudi Lankalapalli Vijaykumar. Synthesizing researches on knowledge management and agile software development using the meta-ethnography method. Journal of Systems and Software, 178:110973, 2021.
Daniela Alves, Deivid Smek, Érica Souza, Katia Felizardo, and Nandamudi Vijaykumar. Updating a systematic literature review on knowledge management diagnostics in software development organizations. In Anais do XXIII Simpósio Brasileiro de Qualidade de Software, Bahia/BA, page 125–135, Porto Alegre, RS, Brasil, 2024. SBC.
Sheikh Abdulaziz Fahad, Said A Salloum, and Khaled Shaalan. The role of chatgpt in knowledge sharing and collaboration within digital workplaces: a systematic review. Artificial intelligence in education: The power and dangers of ChatGPT in the classroom, pages 259–282, 2024.
Tajinder Kumar, Vishal Garg, Sachin Lalar, and Rajinder Kumar. Measuring impact of generative ai in software development and innovation. In International Conference on Entrepreneurship, Innovation, and Leadership, pages 57–67. Springer, 2023.
Juliana dos Santos, Guilherme Augusto Martins, Érica de Souza, Katia Felizardo, and Giovani Meinerz. Perceptions of knowledge management in brazilian software development companies. In XXV Congresso Ibero-Americano em Engenharia de Software, pages 233–247, 2022.
P. Wendorff and D. Apshvalka. The knowledge management strategy of agile software development. pages 607–614, Univ. of Limerick, Ireland, 1998. 6th European Conference on Knowledge Management.
G. A. Ruiz, P. R. da Silva, E. F. de Souza, K. R. Felizardo, G. V. Meinerz, and N. L. Vijaykumar. Knowledge management in agile testing teams: a survey. In 21𝑠𝑡 Ibero-American Conference on Software Engineering (CIbSE’ 18), pages 1–8, 2018.
Victor Cabrero-Daniel et al. Ai for agile development: a meta-analysis. arXiv preprint arXiv:2305.08093, 2023.
Héctor Cornide-Reyes, Diego Monsalves, Esteban Durán, and Jorge Silva. Generative artificial intelligence in agile software development processes: A literature review focused on user experience. In Lecture Notes in Computer Science, volume 15787, pages 242–259. Springer, 2025.
Kai Petersen, Robert Feldt, Shahid Mujtaba, and Michael Mattsson. Systematic mapping studies in software engineering. In International Conference on Evaluation and Assessment in Software Engineering (EASE), pages 1–10, 2008.
B.A. Kitchenham and S. Charters. Guidelines for performing systematic literature reviews in software engineering. Technical report, Keele University and Durham University Joint Report, 2007. Technical Report EBSE 2007–001.
D. Maplesden, E. Tempero, J. Hosking, and J.G. Grundy. Performance analysis for object-oriented software: A systematic mapping. IEEE Transaction on Software Engineering, 41(7):691–710, 2015.
H. Zhang, B.A. Muhammad, and T. Paolo. Identifying relevant studies in software engineering. Information and Software Technology, 53(6):625–637, 2011.
Lili Bo, Yuting He, Xiaobing Sun, Wangjie Ji, and Xiaohan Wu. A software bug fixing approach based on knowledge-enhanced large language models. In 2024 IEEE 24th International Conference on Software Quality, Reliability and Security (QRS), pages 169–179. IEEE, 2024.
Nathan Hagel, Nicolas Hili, and Didier Schwab. Turning low-code development platforms into true no-code with llms. In Proceedings of the ACM/IEEE 27th International Conference on Model Driven Engineering Languages and Systems, pages 876–885, 2024.
Arianna Johnson. Here’s what to know about openai’s ChatGPT — what it’s disrupting and how to use it. Forbes. Acessado em 18 de julho de 2025.
Matthew B. Miles, A. Michael Huberman, and Johnny Saldaña. Qualitative Data Analysis: A Methods Sourcebook. SAGE Publications, Thousand Oaks, CA, 3rd edition, 2014.
Suresh Babu Nettur, Shanthi Karpurapu, Unnati Nettur, and Likhit Sagar Gajja. Cypress copilot: Development of an ai assistant for boosting productivity and transforming web application testing. IEEE Access, 2024.
IEEE Computer Society. Guide to the Software Engineering Body of Knowledge (SWEBOK Guide) – Version 4.0. IEEE, 2024. Accessed: July 2025.
Mafura Uandykova, Laura Baitenova, Gulnar Mukhamejanova, Assel Yeleukulova, and Tolkyn Mirkassimova. Java coding using artificial intelligence. Frontiers in Computer Science, Volume 6 - 2024, 2024.
Rebeka Tóth, Tamas Bisztray, and László Erdődi. Llms in web development: Evaluating llm-generated php code unveiling vulnerabilities and limitations. In International Conference on Computer Safety, Reliability, and Security, pages 425–437. Springer, 2024.
Nenad Petrović, Samir Koničanin, and Suad Suljović. Chatgpt in iot systems: Arduino case studies. In 2023 IEEE 33rd International Conference on Microelectronics (MIEL), pages 1–4. IEEE, 2023.
Saki Imai. Is github copilot a substitute for human pair-programming? an empirical study. In Proceedings of the ACM/IEEE 44th International Conference on Software Engineering: Companion Proceedings, pages 319–321, 2022.
Christian Bird, Denae Ford, Thomas Zimmermann, Nicole Forsgren, Eirini Kalliamvakou, Travis Lowdermilk, and Idan Gazit. Taking flight with copilot. Communications of the ACM, 66(6):56–62, 2023.
Kimiz Dalkir. Knowledge management in theory and practice. routledge, 2013.
Anh Nguyen-Duc and Dron Khanna. Value-based adoption of chatgpt in agile software development: A survey study of nordic software experts. In Generative AI for Effective Software Development, pages 257–273. Springer, 2024.
Antonio Della Porta, Vincenzo De Martino, Gilberto Recupito, Carmine Iemmino, Gemma Catolino, Dario Di Nucci, Fabio Palomba, et al. Using large language models to support software engineering documentation in waterfall life cycles: Are we there yet? In CEUR WORKSHOP PROCEEDINGS, volume 3762, pages 452–457. CEUR-WS, 2024.
Michael J Muller, April Yi Wang, Steven I Ross, Justin D Weisz, Mayank Agarwal, Kartik Talamadupula, Stephanie Houde, Fernando Martinez, John T Richards, Jaimie Drozdal, et al. How data scientists improve generated code documentation in jupyter notebooks. In IUI Workshops, 2021.
Lahbib Naimi, Abdeslam Jakimi, Rachid Saadane, Abdellah Chehri, et al. Automating software documentation: Employing llms for precise use case description. Procedia Computer Science, 246:1346–1354, 2024.
Liuchun Zhan and Changjiang Huang. Research on computer intelligent chatgpt natural language processing system based on scientific knowledge graph. In Proceedings of the 2024 International Conference on Machine Intelligence and Digital Applications, pages 47–51, 2024.
Daksh Chaudhary, Sri Lakshmi Vadlamani, Dimple Thomas, Shiva Nejati, and Mehrdad Sabetzadeh. Developing a llama-based chatbot for ci/cd question answering: A case study at ericsson. In 2024 IEEE International Conference on Software Maintenance and Evolution (ICSME), pages 707–718. IEEE, 2024.
Ningzhi Tang, Meng Chen, Zheng Ning, Aakash Bansal, Yu Huang, Collin McMillan, and Toby Jia-Jun Li. Developer behaviors in validating and repairing llmgenerated code using ide and eye tracking. In 2024 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC), pages 40–46. IEEE, 2024.
Soham Patel, Kailas Patil, and Prawit Chumchu. Bhramari: Bug driven highly reusable automated model for automated test bed generation and integration. Software Impacts, 21:100687, 2024.
Yuxin Qiu, Jie Hu, Qian Zhang, and Heng Yin. Calico: Automated knowledge calibration and diagnosis for elevating ai mastery in code tasks. In Proceedings of the 33rd ACM SIGSOFT International Symposium on Software Testing and Analysis, pages 1785–1797, 2024.
Xin Zhou, Yuqin Jin, He Zhang, Shanshan Li, and Xin Huang. A map of threats to validity of systematic literature reviews in software engineering. In 23rd Asia-Pacific Software Engineering Conferencees, pages 153–160, 2016.
Bianca M. Napoleão, Katia R. Felizardo, Érica F. de Souza, Fabio Petrillo, Sylvain Hallé, Nandamudi L. Vijaykumar, and Elisa Y. Nakagawa. Establishing a search string to detect secondary studies in software engineering. In 47th Euromicro Conference on Software Engineering and Advanced Applications (SEAA), pages 9–16, 2021.
Publicado
04/11/2025
Como Citar
COSTA, Ana Claudia M. P.; FANTINI, Julia Beiroco O.; SOUZA, Érica Ferreira de; BRAGAESILVA, Glaucia; REBELO, Luciana; FELIZARDO, Katia Romero; MEINERZ, Giovani Volnei.
Impact of Generative Artificial Intelligence on Knowledge Management in Software Engineering: A Systematic Mapping Study. In: SIMPÓSIO BRASILEIRO DE QUALIDADE DE SOFTWARE (SBQS), 24. , 2025, São José dos Campos/SP.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 55-66.
DOI: https://doi.org/10.5753/sbqs.2025.13875.
