Capturing Dependencies within Machine Learning via a Formal Process Model

by   Fabian Ritz, et al.

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.


page 6

page 8


Preliminary Systematic Literature Review of Machine Learning System Development Process

Previous machine learning (ML) system development research suggests that...

Quality Assurance Challenges for Machine Learning Software Applications During Software Development Life Cycle Phases

In the past decades, the revolutionary advances of Machine Learning (ML)...

Information FOMO: The unhealthy fear of missing out on information. A method for removing misleading data for healthier models

Not all data are equal. Misleading or unnecessary data can critically hi...

Residual Neural Networks for the Prediction of Planetary Collision Outcomes

Fast and accurate treatment of collisions in the context of modern N-bod...

A Protocol for Intelligible Interaction Between Agents That Learn and Explain

Recent engineering developments have seen the emergence of Machine Learn...

A Computability Perspective on (Verified) Machine Learning

There is a strong consensus that combining the versatility of machine le...

Machine Love

While ML generates much economic value, many of us have problematic rela...

Please sign up or login with your details

Forgot password? Click here to reset