An integrated methodology that combines variability modeling principles from SPLE with architectural modularization concepts is proposed, which bridges variability management and modular architecture design to address increasing system complexity in the automotive industry.
Abstract
Automotive systems are experiencing a rapid increase in complexity driven by the transition towards software-defined vehicles, autonomous functionalities and increasingly interconnected E/E architectures. This transformation intensifies variability across hardware and software domains and challenges established engineering approaches. Traditional modular product development (MPD) provides structural mechanisms to manage hardware complexity, while systems and software product line engineering (SPLE) offers methods for managing software variability. However, these paradigms are typically applied in isolation and lack an integrated methodology capable of addressing cross-domain variability and architectural synchronization in automotive systems. This paper investigates how SPLE and MPD can be systematically integrated to manage variability and architectural complexity in automotive systems. Following a design-oriented research approach, industry requirements are derived from an automotive case study at an OEM. Existing SPLE and modularization approaches are analyzed against these requirements, revealing gaps in cross-domain traceability, synchronization mechanisms, and lifecycle coordination. Based on this analysis, we propose an integrated methodology that combines variability modeling principles from SPLE with architectural modularization concepts. The approach enables management of module structures, supporting system-level consistency in automotive environments. The main contribution is a model-based-integration framework that bridges variability management and modular architecture design to address increasing system complexity in the automotive industry.
A structured framework for cross-domain integration and system-level validation in SDV environments that unifies four foundational engineering capabilities: explicit modeling of inter-domain dependencies, standardized signal interface definitions aligned with AUTOSAR Adaptive specifications, cross-domain temporal synchronization mechanisms, and coordinated scenario-based validation orchestration.
Sumaiyya Fatima· International Journal of Eng...· 0 citations
A dependency-aware OTA orchestration framework that addresses challenges in improving update success rates, efficiency in execution time and update requests through optimized scheduling, and feasibility in maintaining system-wide integrity by successfully reconciling stringent safety requirements and diverse update sensitivity constraints is proposed.
Juyeon Park, In-Young Ko· SIGSOFT FSE Companion· 0 citations
It is demonstrated that multiple architectural views, continuous monitoring, domain-driven decomposition, and AI-assisted design techniques contribute significantly to improving scalability, maintainability, adaptability, and long-term software sustainability.
Dynamic operational environments and accelerating technological disruption are reshaping how systems engineering must be practiced. Traditional lifecycle models assume stability in requirements and context; however, modern socio‐technical systems particularly those incorporating artificial intelligence operate under persistent uncertainty, rapid feedback cycles, and evolving mission demands. This article argues that systems engineering must shift from a control‐centric discipline to one grounded in disciplined adaptability. Integrating agile principles, model‐based systems engineering (MBSE), modular open systems architecture (MOSA), and AI‐enabled capabilities, the paper proposes an adaptive systems engineering operating model for sustained mission relevance. It differentiates the constraints of hardware, software, and AI‐driven systems, examines AI as both engineering tool and system component, and outlines infrastructure requirements including digital threads, continuous validation architectures, and adaptive governance. A cross‐industry aerospace case study demonstrates how modularity, concurrent engineering, and digital twins enable iterative delivery in safety‐critical domains. Practical guidance is provided for systems engineering practitioners seeking to anticipate and respond effectively to dynamic and uncertain operating environments.
The INCOSE future of systems engineering (FuSE) initiative is a collaborative effort to realize the
Systems Engineering Vision 2035
, pivoting from traditional, process‐driven methods to a more agile approach working with the different engineering and systems engineering disciplines, It focuses on addressing complex societal challenges through holistic, sustainable, and AI‐enabled systems engineering, creating actionable roadmaps. The The Agile Systems and Systems Engineering working group has identified eight strategic aspects, two of which are the following:
▪ Shared‐knowledge management – facilitated communication, collaboration, and knowledge curation
▪ Adaptable modular architectures – composable and reconfigurable designs from evolving variations of reusable assets
Model‐based systems engineering (MBSE) has progressed to the point that it is now ubiquitous in systems engineering. It is therefore logical that MBSE be used together with systems engineering agility in order to leverage its benefits. This article will examine how the OMG Reusable Asset Specification (RAS), the Modular Open Systems Approach (MOSA), product line engineering (PLE) used as part of MBSE, and agile can address these strategic aspects to enable systems engineering agility.
Early assurance of properties in complex technical systems can reduce engineering effort and even entire iterations. However, verification and validation face challenges caused by interdisciplinary dependencies and late availability of prototypes. Although Model-Based Systems Engineering offers formal system models for representing architectures, their potential for early test planning and prototype definition remains insufficiently used. This article presents a process chain for systematically extracting test-relevant system elements and parameters from system models to support V&V engineers in deriving suitable prototype configurations. Based on a systematic literature analysis, success criteria are defined and translated into a four-step approach: selecting the properties to be verified, automatically identifying relevant system elements, extracting test-relevant parameters, and generating prototype configurations. The process chain is tool-supported integrated. Relevant elements and parameters are exported from the system model using Node-RED flows, merged into a template structure, and visualized in a dashboard. The evaluation using a EU research project as well as a wind turbine case indicates potential for time savings, improved test coverage, enhanced traceability, and earlier detection of inconsistencies.