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AI-Driven Assistants' Potential for Scaled Agile Software Development

Author

Listed:
  • Muhammad Hamza

    (Department of Software Engineering, Superior University, Lahore, Pakistan)

  • Muhammad Waseem Iqbal

    (Associate Professor, Department of Software Engineering, Superior University, Lahore, Pakistan)

  • Saleem Zubair Ahmad

    (Professor Department of Software Engineering, Superior University, Lahore, Pakistan)

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.

Suggested Citation

  • Muhammad Hamza & Muhammad Waseem Iqbal & Saleem Zubair Ahmad, 2024. "AI-Driven Assistants' Potential for Scaled Agile Software Development," Bulletin of Business and Economics (BBE), Research Foundation for Humanity (RFH), vol. 13(2), pages 974-982.
    • Handle: RePEc:rfh:bbejor:v:13:y:2024:i:2:p:974-982
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