AI-Assisted Development Tools and Team Dynamics in South African Software Engineering Teams

Authors

  • Bassey Isong North-West University, South Africa
  • Tshipuke Vhahangwele North-West University, South Africa
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DOI:

https://doi.org/10.63158/journalisi.v8i3.1615

Keywords:

AI-assisted development tools, GitHub Copilot, software team dynamics, software delivery outcomes, South African software development

Abstract

AI-assisted development tools are widely adopted in software engineering (SE), yet their effects on team dynamics and software delivery outcomes remain poorly understood in sub-Saharan African settings. This paper investigates how AI tool integration influences team roles, collaboration, skill requirements, and software delivery outcomes among South African software development professionals. A mixed-methods design was used, combining a structured survey with thematic analysis of open-ended responses from 40 participants across developer, tester, DevOps, and team lead roles. Multiple linear regression and Spearman's rank correlation were applied to quantitative data; thematic analysis followed the six-phase approach of Braun and Clarke. Findings show that GitHub Copilot was used by 75% of respondents. Interpersonal trust was the strongest predictor of development speed (β = 0.485, p = 0.017), exceeding all AI-specific variables in the model. AI use at the adoption onset reduced development speed; frequency of use increased it. Role transformation was reported by 95% of respondents and predicted team productivity. However, causal inference is not warranted given the cross-sectional design and reliance on self-reported measures. The findings are further constrained by a purposive sample of 40 drawn from networked professional communities, which limits statistical power and generalisability. To the authors’ knowledge, no prior published study has examined AI adoption and team dynamics within a South African SE population, using this combination of methods, though a systematic literature review of that literature was beyond the scope of this study.

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References

[1] N. Gupta, “The Rise of AI Copilots: Redefining Human-Machine Collaboration in Knowledge Work,” Int. J. Humanities Inf. Technol., vol. 7, no. 3, 2025. https://doi.org/10.21590/

[2] S. Panyam and P. Gujar, “How AI Agents Are Transforming Software Engineering and the Future of Product Development,” Computer, vol. 58, no. 5, pp. 71–77, May 2025, doi: 10.1109/MC.2024.3488378.

[3] B. Tabarsi et al., “LLMs' Reshaping of People, Processes, Products, and Society in Software Development: A Comprehensive Exploration with Early Adopters,” arXiv, preprint. arXiv:2503.05012, 2025.

[4] S. Sarkar, “The Effect of AI Tools on Modern Software Development for Frontend Engineering: An Empirical Analysis,” SSRN Electronic Journal, Jan. 2025, doi: 10.2139/ssrn.5442494.

[5] R. Mo et al., “Assessing and Analyzing the Correctness of GitHub Copilot’s Code Suggestions,” ACM Trans. Softw. Eng. Methodol., vol. 34, no. 7, Art. no. 194, Sep. 2025, doi: 10.1145/3715108.

[6] C. Johnson et al., “The AI Productivity Paradox in Software Development: A Meta-Analysis of Implementation Outcomes and ROI Patterns (2024-2025),” SSRN Electronic Journal, 2025, doi: 10.2139/ssrn.5902163.

[7] Z. S. Li, N. N. Arony, A. M. Awon, D. Damian, and B. Xu, “AI Tool Use and Adoption in Software Development by Individuals and Organizations: A Grounded Theory Study,” arXiv, preprint, arXiv:2406.17325, 2024.

[8] M. O. Ahmad, H. Ghanbari, T. Gustavsson, and B. R. Upreti, “It all starts with structure: Investigating learning dynamics in large-scale agile software development,” J. Syst. Softw., vol. 230, p. 112561, 2025, doi: 10.1016/j.jss.2025.112561.

[9] L. M. Restrepo-Tamayo, G. P. Gasca-Hurtado, and J. Valencia-Calvo, “Characterizing Social and Human Factors in Software Development Team Productivity: A System Dynamics Approach,” IEEE Access, vol. 12, pp. 59739–59755, 2024, doi: 10.1109/ACCESS.2024.3388505.

[10] T. Felder et al., “Adoption of Generative Artificial Intelligence in the German Software Engineering Industry: An Empirical Study,” arXiv, preprint, arXiv:2601.16700, 2026.

[11] R. Tomaz, P. Guenes, A. A. Araújo, M. T. Baldassarre, and M. Kalinowski, “Impacts of Generative AI on Agile Teams' Productivity: A Multi-Case Longitudinal Study,” arXiv, preprint, arXiv:2602.13766, 2026.

[12] S. Maatta, “How Do Programmers Evaluate AI-Generated Code?” in Proc. 2025 ACM/IEEE Int. Symp. Empirical Softw. Eng. Meas. (ESEM), Honolulu, HI, USA, 2025, pp. 522–527, doi: 10.1109/ESEM64174.2025.00057.

[13] T. Vhahangwele, B. Isong, and A. A. Mahfouz, “The Impact of Team Dynamics on Software Quality and Productivity: Evidence from South Africa,” Journalisi, vol. 8, no. 1, pp. 809–840, Mar. 2026, doi: 10.63158/journalisi.v8i1.1438.

[14] P. Diebold, “From backlogs to bots: Generative AI's impact on agile role evolution,” J. Softw.: Evol. Process, vol. 37, no. 1, p. e2740, 2025, doi: 10.1002/smr.2740.

[15] J. Zhou et al., “Exploring the problems, their causes and solutions of AI pair programming: A study on GitHub and Stack Overflow,” J. Syst. Softw., vol. 219, p. 112204, 2025, doi: 10.1016/j.jss.2024.112204.

[16] W. Mendes, S. Souza, and C. de Souza, “You're on a bicycle with a little motor”: Benefits and Challenges of Using AI Code Assistants,” in Proc. 2024 IEEE/ACM 17th Int. Conf. Cooperative Hum. Aspects Softw. Eng. (CHASE), Lisbon, Portugal, 2024, pp. 45–56, doi: 10.1145/3643750.3643758.

[17] V. Stray, A. Barbala, and V. T. Wivestad, “Human-AI Collaboration in Software Development: A Mixed-Methods Study of Developers’ Use of GitHub Copilot and ChatGPT,” in Companion Proc. 33rd ACM Int. Conf. Foundations Softw. Eng. (FSE Companion '25), New York, NY, USA: ACM, 2025, pp. 1325–1332, doi: 10.1145/3696630.3730566.

[18] S. Ferino, R. Hoda, J. Grundy, and C. Treude, “Walking the Tightrope of LLMs for Software Development: A Practitioners' Perspective,” arXiv, preprint, arXiv:2511.06428, 2025.

[19] A. Welter et al., “An Empirical Study of Knowledge Transfer in AI Pair Programming,” in Proc. 2025 40th IEEE/ACM Int. Conf. Autom. Softw. Eng. (ASE), 2025, pp. 210–222, doi: 10.1109/ASE62340.2025.00021.

[20] T. Chen, “The impact of AI-pair programmers on code quality and developer satisfaction: Evidence from Timi Studio,” in Proc. 2024 Int. Conf. Generative Artif. Intell. Inf. Secur. (GAIIS), 2024, pp. 88–94, doi: 10.1109/GAIIS62410.2024.00015.

[21] D. Shao and F. Ishengoma, “Empirical Analysis of Generative AI Tool Adoption in Software Development,” Inf. Softw. Technol., vol. 182, p. 108036, Jun. 2026, doi: 10.1016/j.infsof.2025.108036.

[22] A. Kumar et al., “Intuition to Evidence: Measuring AI's True Impact on Developer Productivity,” arXiv, arXiv:2509.19708, 2025.

[23] E. Paradis et al., “How much does AI impact development speed? An enterprise-based randomized controlled trial,” in Proc. 2025 IEEE/ACM 47th Int. Conf. Softw. Eng.: Softw. Eng. Practice (ICSE-SEIP), 2025, pp. 312–323, doi: 10.1109/ICSE-SEIP64321.2025.00034.

[24] M. Muñoz et al., “Comparative study of AI code generation tools: Quality assessment and performance analysis,” Lat. Am. J. Inf. Technol. (LatIA), vol. 2, pp. 104–104, 2024.

[25] M. E. Souza and E. C. S. Murillo, “How Generative Artificial Intelligence Tools Can Improve the Quality of Software Produced by Software Development Teams,” J. Softw. Eng. Res. Dev., vol. 13, no. 1, pp. 45–58, 2025, doi: 10.5753/jserd.2025.4125.

[26] V. Braun and V. Clarke, “Using thematic analysis in psychology,” Qual. Res. Psychol., vol. 3, no. 2, pp. 77–101, 2006, doi: 10.1191/1478088706qp063oa.

[27] S. K. Ahmed et al., “Using thematic analysis in qualitative research,” J. Med., Surgery, Public Health, vol. 6, p. 100198, 2025, doi: 10.1016/j.glmedi.2025.100198.

[28] P. Hoffmann, D. Zercher, and T. C. Schimmer, “How AI communication capabilities affect developer productivity in AI pair programming: Insights from a large software development organization,” in Research-in-Progress Papers, European Conference on Information Systems (ECIS 2025 TREOs), Paper 23, 2025. [Online]. Available: https://aisel.aisnet.org/treos_ecis2025/23

[29] V. V. Jensen, A. Alami, A. R. Bruun, et al., “Managing expectations towards AI tools for software development: a multiple-case study,” Inf. Syst. E-Bus. Manage., vol. 23, pp. 869–901, 2025, doi: 10.1007/s10257-025-00704-7.

[30] K. Koyanagi et al., “Exploring the effect of multiple natural languages on code suggestion using GitHub Copilot,” in Proc. 21st Int. Conf. Min. Softw. Repositories (MSR), Lisbon, Portugal, 2024, pp. 115–126, doi: 10.1145/3643990.3644012.

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Published

2026-06-25

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