Quantifying Long-Term Evolution of Industrial Meta-Models - A Case Study

Measurement in software engineering is an important activity for successful planning and management of projects under development. However knowing what to measure and how is crucial for the correct interpretation of the measurement results. In this paper, we assess the applicability of a number of software metrics for measuring a set of meta-model properties - size, length, complexity, coupling and cohesion. The goal is to identify which of these properties are mostly affected by the evolution of industrial meta-models and also which metrics should be used for their successful monitoring. In order to assess the applicability of the chosen set of metrics, we calculate them on a set of releases of the standardized meta-model used in the development of automotive software systems - the AUTOSAR meta-model - in a case study at Volvo Car Corporation. To identify the most applicable metrics, we used Principal Component Analysis (PCA). The results of these metrics shall be used by software designers in planning software development projects based on multiple AUTOSAR meta-model versions. We concluded that the evolution of the AUTOSAR meta-model is quite even with respect to all 5 properties and that the metrics based on fan-in complexity and package cohesion quantify the evolution most accurately.