AI-Driven Assistants' Potential for Scaled Agile Software Development

Authors

  • Muhammad Hamza Department of Software Engineering, Superior University, Lahore, Pakistan Author
  • Muhammad Waseem Iqbal Associate Professor, Department of Software Engineering, Superior University, Lahore, Pakistan Author
  • Saleem Zubair Ahmad Professor Department of Software Engineering, Superior University, Lahore, Pakistan Author

DOI:

https://doi.org/10.61506/01.00416

Keywords:

SAFe; scaled agile framework; AI; artificial intelligence; tools; assistants; agile; large-scale

Abstract

Scaled agile development is commonly used in software engineering to enhance cooperation, productivity, and product quality. Incorporating artificial intelligence (AI) into scaled agile development methods (SADMs) is a promising way to simplify procedures and manage the complexity of software projects. This article examines the impact of AI-powered assistants on the scaled agile framework (SAFe), a popular paradigm for large-scale software development. Our article targets three main objectives: (1) Assessing the obstacles and constraints organizations face while implementing SADMs (2) evaluating the benefits of AI in large-scale situations, and (3) identifying features of SADMs that AI-driven assistants may improve. After conducting a thorough literature analysis, we identified and summarized 18 key difficulties organizations face. Our research identified seven benefits and five barriers to using AI in SADMs. The findings were categorized according to whether they occurred during the development or planning and control stages. We analyzed 15 AI helpers and tools and used them to meet research issues. The findings were categorized according to whether they occurred during the development or planning and control stages. We analyzed 15 AI helpers and tools and used them to meet research issues.

References

Ameta, U., Patel, M., & Sharma, A. K. (2022, October). Scaled Agile Framework Implementation in Organizations', its Shortcomings and an AI Based Solution to Track Team's Performance. In 2022 IEEE 3rd Global Conference for Advancement in Technology (GCAT) (pp. 1-7). IEEE. DOI: https://doi.org/10.1109/GCAT55367.2022.9971968

Batarseh, F. A., & Gonzalez, A. J. (2018). Predicting failures in agile software development through data analytics. Software Quality Journal, 26, 49-66. DOI: https://doi.org/10.1007/s11219-015-9285-3

Brühl, V. (2022). Agile methods in the German banking sector: some evidence on expectations, experiences and success factors. Journal of business economics, 92(8), 1337-1372. DOI: https://doi.org/10.1007/s11573-022-01102-y

Carleton, A. D., Harper, E., Menzies, T., Xie, T., Eldh, S., & Lyu, M. R. (2020). The AI Effect: Working at the Intersection of AI and SE. IEEE Software, 37(4), 26-35. DOI: https://doi.org/10.1109/MS.2020.2987666

Ciancarini, P., Kruglov, A., Pedrycz, W., Salikhov, D., & Succi, G. (2022, May). Issues in the adoption of the scaled agile framework. In Proceedings of the 44th international conference on software engineering: software engineering in practice (pp. 175-184). DOI: https://doi.org/10.1145/3510457.3513028

Conboy, K., & Carroll, N. (2019). Implementing large-scale agile frameworks: challenges and recommendations. IEEE software, 36(2), 44-50. DOI: https://doi.org/10.1109/MS.2018.2884865

Dam, H. K. (2019). Empowering software engineering with artificial intelligence. In Service Research and Innovation: 7th Australian Symposium, ASSRI 2018, Sydney, NSW, Australia, September 6, 2018, and Wollongong, NSW, Australia, December 14, 2018, Revised Selected Papers 7 (pp. 22-32). Springer International Publishing. DOI: https://doi.org/10.1007/978-3-030-32242-7_3

Edison, H., Wang, X., & Conboy, K. (2021). Comparing methods for large-scale agile software development: A systematic literature review. IEEE Transactions on Software Engineering, 48(8), 2709-2731. DOI: https://doi.org/10.1109/TSE.2021.3069039

Elbasheer, M., Longo, F., Nicoletti, L., Padovano, A., Solina, V., & Vetrano, M. (2022). Applications of ML/AI for decision-intensive tasks in production planning and control. Procedia Computer Science, 200, 1903-1912. DOI: https://doi.org/10.1016/j.procs.2022.01.391

Fisman, D., & Rosu, G. (2022). Tools and Algorithms for the Construction and Analysis of Systems: 28th International Conference, TACAS 2022, Held as Part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2022, Munich, Germany, April 2–7, 2022, Proceedings, Part I (p. 583). Springer Nature.

Fountaine, T., McCarthy, B., & Saleh, T. (2019). Building the AI-powered organization. Harvard Business Review, 97(4), 62-73.

Fucci, D., Palomares, C., Franch, X., Costal, D., Raatikainen, M., Stettinger, M., ... & Maalej, W. (2018, October). Needs and challenges for a platform to support large-scale requirements engineering: A multiple-case study. In Proceedings of the 12th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (pp. 1-10). DOI: https://doi.org/10.1145/3239235.3240498

Jarrahi, M. H. (2018). Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making. Business horizons, 61(4), 577-586. DOI: https://doi.org/10.1016/j.bushor.2018.03.007

Järvinen, M. (2023). The Benefits and Challenges of Scaled Agile Framework in the IT Industry-case study: company x.

Kasauli, R., Knauss, E., Horkoff, J., Liebel, G., & de Oliveira Neto, F. G. (2021). Requirements engineering challenges and practices in large-scale agile system development. Journal of Systems and Software, 172, 110851. DOI: https://doi.org/10.1016/j.jss.2020.110851

Kitchenham, B. A. (2012, September). Systematic review in software engineering: where we are and where we should be going. In Proceedings of the 2nd international workshop on Evidential assessment of software technologies (pp. 1-2). DOI: https://doi.org/10.1145/2372233.2372235

Limaj, E., & Bernroider, E. W. (2022). A taxonomy of scaling agility. The journal of strategic information systems, 31(3), 101721. DOI: https://doi.org/10.1016/j.jsis.2022.101721

Lu, M., & Qiu, J. L. (2022). Empowerment or warfare? dark skin, AI camera, and Transsion’s patent narratives. Information, Communication & Society, 25(6), 768-784. DOI: https://doi.org/10.1080/1369118X.2022.2056500

Mancl, D., & Fraser, S. D. (2021, June). The Future of Software Engineering: Where Will Machine Learning, Agile, and Virtualization Take Us Next?. In International Conference on Agile Software Development (pp. 222-230). Cham: Springer International Publishing. DOI: https://doi.org/10.1007/978-3-030-88583-0_23

Mikalef, P., & Gupta, M. (2021). Artificial intelligence capability: Conceptualization, measurement calibration, and empirical study on its impact on organizational creativity and firm performance. Information & management, 58(3), 103434. DOI: https://doi.org/10.1016/j.im.2021.103434

Mosqueira-Rey, E., Pereira, E. H., Alonso-Ríos, D., & Bobes-Bascarán, J. (2022, April). A classification and review of tools for developing and interacting with machine learning systems. In Proceedings of the 37th ACM/SIGAPP Symposium on Applied Computing (pp. 1092-1101). DOI: https://doi.org/10.1145/3477314.3507310

Peng, X., Xing, Z., & Sun, J. (2019). AI-boosted software automation: learning from human pair programmers. Science China. Information Sciences, 62(10), 200104. DOI: https://doi.org/10.1007/s11432-018-9854-3

Ploennigs, J., & Berger, M. (2023). AI art in architecture. AI in Civil Engineering, 2(1), 8. DOI: https://doi.org/10.1007/s43503-023-00018-y

Putta, A., Uludağ, Ö., Hong, S. L., Paasivaara, M., & Lassenius, C. (2021, October). Why do organizations adopt agile scaling frameworks? a survey of practitioners. In Proceedings of the 15th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM) (pp. 1-12). DOI: https://doi.org/10.1145/3475716.3475788

Ramadhina, F. A., & Raharjo, T. (2023). Scaled Agile Method Design: Case Study of a SaaS Company in Indonesia. J-Icon: Jurnal Komputer dan Informatika, 11(2), 165-173. DOI: https://doi.org/10.35508/jicon.v11i2.12218

Saklamaeva, V., & Pavlič, L. (2023). The potential of ai-driven assistants in scaled agile software development. Applied Sciences, 14(1), 319. DOI: https://doi.org/10.3390/app14010319

Sinha, R., Shameem, M., & Kumar, C. (2020, February). SWOT: Strength, weaknesses, opportunities, and threats for scaling agile methods in global software development. In Proceedings of the 13th Innovations in Software Engineering Conference (formerly known as India Software Engineering Conference) (pp. 1-10). DOI: https://doi.org/10.1145/3385032.3385037

Song, B., Gyory, J. T., Zhang, G., Zurita, N. F. S., Stump, G., Martin, J., ... & Cagan, J. (2022). Decoding the agility of artificial intelligence-assisted human design teams. Design Studies, 79, 101094. DOI: https://doi.org/10.1016/j.destud.2022.101094

Song, B., Zurita, N. S., Zhang, G., Stump, G., Balon, C., Miller, S. W., ... & McComb, C. (2020, May). Toward hybrid teams: A platform to understand human-computer collaboration during the design of complex engineered systems. In Proceedings of the design society: DESIGN conference (Vol. 1, pp. 1551-1560). Cambridge University Press. DOI: https://doi.org/10.1017/dsd.2020.68

Uludağ, Ö., Harders, N. M., & Matthes, F. (2019, July). Documenting recurring concerns and patterns in large-scale agile development. In Proceedings of the 24th European Conference on Pattern Languages of Programs (pp. 1-17). DOI: https://doi.org/10.1145/3361149.3361176

Uludag, Ö., Kleehaus, M., Caprano, C., & Matthes, F. (2018, October). Identifying and structuring challenges in large-scale agile development based on a structured literature review. In 2018 IEEE 22nd international enterprise distributed object computing conference (EDOC) (pp. 191-197). IEEE. DOI: https://doi.org/10.1109/EDOC.2018.00032

Zhang, G., Raina, A., Cagan, J., & McComb, C. (2021). A cautionary tale about the impact of AI on human design teams. Design Studies, 72, 100990. DOI: https://doi.org/10.1016/j.destud.2021.100990

Zimmermann, A., Schmidt, R., & Sandkuhl, K. (2020). Strategic challenges for platform-based intelligent assistants. Procedia computer science, 176, 966-975. DOI: https://doi.org/10.1016/j.procs.2020.09.092

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Published

2024-06-01

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Articles

How to Cite

Hamza, M. ., Iqbal, M. W. ., & Ahmad, S. Z. . (2024). AI-Driven Assistants’ Potential for Scaled Agile Software Development. Bulletin of Business and Economics (BBE), 13(2), 974-982. https://doi.org/10.61506/01.00416