Feature Interactions on Steroids: On the Composition of ML Models

kaestner@cs.cmu.edu (C. Kästner, E. Kang, S. Apel)
Thu, 20 May 2021 10:20:39 GMT

Christian Kästner, Eunsuk Kang, and Sven Apel. Feature Interactions on Steroids: On the Composition of ML Models. Technical Report 2105.06449, arXiv, May 2021.

The lack of specifications is a key difference between traditional software engineering and machine learning. We discuss how it drastically impacts how we think about divide-and-conquer approaches to system design, and how it impacts reuse, testing and debugging activities. Traditionally, specifications provide a cornerstone for compositional reasoning and for the divide-and-conquer strategy of how we build large and complex systems from components, but those are hard to come by for machine-learned components. While the lack of specification seems like a fundamental new problem at first sight, in fact software engineers routinely deal with iffy specifications in practice: we face weak specifications, wrong specifications, and unanticipated interactions among components and their specifications. Machine learning may push us further, but the problems are not fundamentally new. Rethinking machine-learning model composition from the perspective of the feature interaction problem, we may even teach us a thing or two on how to move forward, including the importance of integration testing, of requirements engineering, and of design.