| Abstrakt: |
The characteristics of AI-based software have the potential to reshape traditional software development paradigms. Consequently, this study conducts a systematic literature review (SLR) within the field of AI Engineering to identify the unique challenges in software engineering for AI-based systems, which are transforming traditional software development paradigms. The scope of the SLR includes literature from academic journals and conference proceedings published between 2018 and 2023, selected through a rigorous process. The methodology involved using specific search keywords across databases such as Scopus, ScienceDirect, ACM Digital Library, and IEEE Xplore, with a stringent application of Kitchenham's inclusion and exclusion criteria to ensure a focused and relevant review. This review provides a consolidated summary of diverse research endeavors addressing challenges, issues, and methodologies relevant to AI-based software development. Highlighted topics encompass challenges in requirements engineering for AI-intensive system development, responsible software development (responsible AI), the formulation of a software engineering roadmap for responsible AI, the application of TrustOps as a risk management methodology in AI system development, the necessity of incorporating software engineering methods in AI-based systems, as well as studies exploring requirements engineering practices, AI-intensive system development, and the utilization of tools in machine learning model development. Key findings include the importance of recognizing ethical requirements in AI development, the role of risk management and ethical attributes, and the challenges of connecting requirements between software developers, data scientists, and machine learning specialists. This research provides valuable insights for practitioners and researchers involved in developing AI-based software to overcome existing challenges and apply appropriate methods in the development process. [ABSTRACT FROM AUTHOR] |
