| Title: | The potential of ai-driven assistants in scaled agile software development |
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| Authors: | ID Saklamaeva, Vasilka (Author)
ID Pavlič, Luka (Author) |
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| Files: |  applsci-14-00319-v2.pdf (816,79 KB)
MD5: F2D6DE3DA5F5A04A7825F3BFF5CBDABC
 https://doi.org/10.3390/app14010319
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| Language: | English |
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| Work type: | Article |
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| Typology: | 1.01 - Original Scientific Article |
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| Organization: | FERI - Faculty of Electrical Engineering and Computer Science
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| Abstract: | Scaled agile development approaches are now used widely in modern software engineering, allowing businesses to improve teamwork, productivity, and product quality. The incorporation of artificial intelligence (AI) into scaled agile development methods (SADMs) has emerged as a potential strategy in response to the ongoing demand for simplified procedures and the increasing complexity of software projects. This paper explores the intersection of AI-driven assistants within the context of the scaled agile framework (SAFe) for large-scale software development, as it stands out as the most widely adopted framework. Our paper pursues three principal objectives: (1) an evaluation of the challenges and impediments encountered by organizations during the implementation of SADMs, (2) an assessment of the potential advantages stemming from the incorporation of AI in large-scale contexts, and (3) the compilation of aspects of SADMs that AI-driven assistants enhance. Through a comprehensive systematic literature review, we identified and described 18 distinct challenges that organizations confront. In the course of our research, we pinpointed seven benefits and five challenges associated with the implementation of AI in SADMs. These findings were systematically categorized based on their occurrence either within the development phase or the phases encompassing planning and control. Furthermore, we compiled a list of 15 different AI-driven assistants and tools, subjecting them to a more detailed examination, and employing them to address the challenges we uncovered during our research. One of the key takeaways from this paper is the exceptional versatility and effectiveness of AI-driven assistants, demonstrating their capability to tackle a broader spectrum of problems. In conclusion, this paper not only sheds light on the transformative potential of AI, but also provides invaluable insights for organizations aiming to enhance their agility and management capabilities. |
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| Keywords: | SAFe, scaled agile framework, AI, artificial intelligence, tools, assistants, agile, large-scale |
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| Publication status: | Published |
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| Publication version: | Version of Record |
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| Submitted for review: | 08.11.2023 |
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| Article acceptance date: | 22.12.2023 |
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| Publication date: | 29.12.2023 |
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| Publisher: | MDPI |
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| Year of publishing: | 2024 |
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| Number of pages: | 20 str. |
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| Numbering: | Vol. 14, iss. 1, [article no.] 319 |
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| PID: | 20.500.12556/DKUM-86892  |
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| UDC: | 004.8 |
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| ISSN on article: | 2076-3417 |
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| COBISS.SI-ID: | 179571203 |
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| DOI: | 10.3390/app14010319 |
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| Copyright: | © 2023 by the authors |
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| Publication date in DKUM: | 26.01.2024 |
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| Views: | 544 |
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| Downloads: | 138 |
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| Metadata: |    |
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| Categories: | Misc.
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