Capturing Dependencies within Machine Learning via a Formal Process Model

TL;DR

This paper presents a formal software development process model tailored for machine learning, capturing dependencies, lifecycle evolution, and enabling formal reasoning and automation for self-adaptive systems.

Contribution

It introduces a comprehensive, formal SD process model for ML that integrates lifecycle management, specifications, and optimization, facilitating automation and formal reasoning.

Findings

Provides a formal process model encompassing ML tasks and artifacts.

Enables reasoning about ML development progress over time.

Supports self-adaptive autonomous systems through formal optimization.

Abstract

The development of Machine Learning (ML) models is more than just a special case of software development (SD): ML models acquire properties and fulfill requirements even without direct human interaction in a seemingly uncontrollable manner. Nonetheless, the underlying processes can be described in a formal way. We define a comprehensive SD process model for ML that encompasses most tasks and artifacts described in the literature in a consistent way. In addition to the production of the necessary artifacts, we also focus on generating and validating fitting descriptions in the form of specifications. We stress the importance of further evolving the ML model throughout its life-cycle even after initial training and testing. Thus, we provide various interaction points with standard SD processes in which ML often is an encapsulated task. Further, our SD process model allows to formulate ML as a (meta-) optimization problem. If automated rigorously, it can be used to realize self-adaptive autonomous systems. Finally, our SD process model features a description of time that allows to reason about the progress within ML development processes. This might lead to further applications of formal methods within the field of ML.