State-of-the-Art and Challenges of Engineering ML- Enabled Software Systems in the Deep Learning Era

Abstract

Emerging from the software crisis of the 1960s, conventional software systems have vastly improved through Software Engineering (SE) practices. Simultaneously, Artiicial Intelligence (AI) endeavors to augment or replace human decision- making. In the contemporary landscape, Machine Learning (ML), a subset of AI, leverages extensive data from diverse sources, fostering the development of ML-enabled (intelligent) software systems. While ML is increasingly utilized in conventional software development, the integration of SE practices in developing ML-enabled systems, especially across typical Software Development Life Cycle (SDLC) phases and methodologies in the post-2010 Deep Learning (DL) era, remains underexplored. Our survey of existing literature unveils insights into current practices, emphasizing the interdisciplinary collaboration challenges of developing ML-enabled software, including data quality, ethics, explainability, continuous monitoring and adaptation, and security. The study underscores the imperative for ongoing research and development with focus on data- driven hypotheses, non-functional requirements, established design principles, ML-irst integration, automation, specialized testing, and use of agile methods.