A Product Line Systems Engineering Process for Variability Identification and Reduction

TL;DR

This paper introduces an automated Product Line Systems Engineering process that leverages hierarchical variability modeling and legacy system analysis to reduce variation points in large-scale system development, demonstrated on aerospace and other domain case studies.

Contribution

It extends the System Orthogonal Variability Model with formal hierarchical definitions and integrates legacy system models to identify and reduce variation points automatically.

Findings

Reduction of 14% to 40% in variation points across case studies.

Demonstrated applicability in aerospace and other engineering domains.

Automated process supports large-scale, multi-disciplinary system product line engineering.

Abstract

Software Product Line Engineering has attracted attention in the last two decades due to its promising capabilities to reduce costs and time to market through reuse of requirements and components. In practice, developing system level product lines in a large-scale company is not an easy task as there may be thousands of variants and multiple disciplines involved. The manual reuse of legacy system models at domain engineering to build reusable system libraries and configurations of variants to derive target products can be infeasible. To tackle this challenge, a Product Line Systems Engineering process is proposed. Specifically, the process extends research in the System Orthogonal Variability Model to support hierarchical variability modeling with formal definitions; utilizes Systems Engineering concepts and legacy system models to build the hierarchy for the variability model and to identify essential relations between variants; and finally, analyzes the identified relations to reduce the number of variation points. The process, which is automated by computational algorithms, is demonstrated through an illustrative example on generalized Rolls-Royce aircraft engine control systems. To evaluate the effectiveness of the process in the reduction of variation points, it is further applied to case studies in different engineering domains at different levels of complexity. Subject to system model availability, reduction of 14% to 40% in the number of variation points are demonstrated in the case studies.