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Continuous Software Engineering Practices in AI/ML Development Past the Narrow Lens of MLOps: Adoption Challenges

Vänskä Sini, Kemell Kai-Kristian, Mikkonen Tommi, Abrahamsson Pekka

e-Informatica Software Engineering Journal

2024

Vol. 18, nr 1

240102

Background: Continuous software engineering practices are currently considered state of the art in Software Engineering (SE). Recently, this interest in continuous SE has extended to ML system development as well, primarily through MLOps. However, little is known about continuous SE in ML development outside the specific continuous practices present in MLOps. Aim: In this paper, we explored continuous SE in ML development more generally, outside the specific scope of MLOps. We sought to understand what challenges organizations face in adopting all the 13 continuous SE practices identified in existing literature. Method: We conducted a multiple case study of organizations developing ML systems. Data from the cases was collected through thematic interviews. The interview instrument focused on different aspects of continuous SE, as well as the use of relevant tools and methods. Results: We interviewed 8 ML experts from different organizations. Based on the data, we identified various challenges associated with the adoption of continuous SE practices in ML development. Our results are summarized through 7 key findings. Conclusion: The largest challenges we identified seem to stem from communication issues. ML experts seem to continue to work in silos, detached from both the rest of the project and the customers.

Measurement and Impact Factors of Speed of Reviews and Integration in Continuous Software Engineering

Staron M., Meding W., Söder O., Bäck M.

Foundations of Computing and Decision Sciences

2018

Vol. 43, No. 4

281--303

Continuous integration and continuous software deployment depend on the mix of automated and manual activities. The automated build and test processes are often intertwined with manual reviews and bug-fixing activities. In this paper, we set o to study how these manual and automated activities influence the speed of reviews and integration. We conduct a case study of two companies developing embedded software, measure the time required for reviewing and integrating software code (alias speed), and conduct a workshop to identify factors which explain the quantitative results. Our results show that the measurement of speed is a good alias for calendar time and triggers improvements better than using measures for velocity. We have also found that the distribution of code repositories, frequent reminders and team proximity decrease the time needed to deploy the software. Our findings are that there is a difference in the structure of code repositories between the fast and slow integration cases, which contributes to the debate on the pros and cons of different repository structures in modern companies.