Architecture modeling software analytics: Model quality and maturity assessment using automated tools

摘要

This paper presents a new collection of architecture modeling software analytics for identification of common errors and omissions committed by both new and experienced users of model-based engineering tools. These analytics are based on architecture modeling heuristics that have been gained through the author's past 15 years of modeling experience and practice, and are novel in that they can be expressed in a multitude of conceptual modeling languages making them modeling tool-agnostic. The analytics enable students and practitioners to quickly and automatically identify typical architecture model properties impacting model quality and maturity and correct them, enabling time formerly spent on the labor-intensive and error-prone chore of manually finding and fixing the issues to be reinvested in more advanced tasks, such as conducting verification and validation of the design described by the model. The paper includes some examples, a summary of conclusions, and next steps towards improving and deploying these analytics to relieve more humans of the manually intense labor of checking models for properties that impact quality and maturity.