Big companies use many tools, jointly referred to as the toolchain, to manage vast amounts of engineering data being generated across an application lifecycle. Individual tools are typically designed to perform specific engineering tasks, and rely on specific data formats. This leads to problems when attempting to automate engineering tasks that are not supported by a particular tool, and which require data from multiple tools. This paper presents the experiences and lessons learned from an industrial research-project within the heavy vehicle manufacturer Scania, where the project goal was to identify and industrialize technologies and principles that solve the above problem. The presented lessons cover architectural, technological, and organizational aspects of a toolchain development-process. In addition, as a consequence of the lessons learned, the toolchain architecture and tool-interface architecture is also presented.

Feature models are arguably one of the most intuitive and successful notations for modeling the features of a variant-rich software system. Feature models help developers to keep an overall understanding of the system, and also support scoping, planning, development, variant derivation, configuration, and maintenance activities that sustain the system's long-term success. Unfortunately, feature models are difficult to build and evolve. Features need to be identified, grouped, organized in a hierarchy, and mapped to software assets. Also, dependencies between features need to be declared. While feature models have been the subject of three decades of research, resulting in many feature-modeling notations together with automated analysis and configuration techniques, a generic set of principles for engineering feature models is still missing. It is not even clear whether feature models could be engineered using recurrent principles. Our work shows that such principles in fact exist. We analyzed feature-modeling practices elicited from ten interviews conducted with industrial practitioners and from 31 relevant papers. We synthesized a set of 34 principles covering eight different phases of feature modeling, from planning over model construction, to model maintenance and evolution. Grounded in empirical evidence, these principles provide practical, context-specific advice on how to perform feature modeling, describe what information sources to consider, and highlight common characteristics of feature models. We believe that our principles can support researchers and practitioners enhancing feature-modeling tooling, synthesis, and analyses techniques, as well as scope future research.

Highly configurable systems are complex pieces of software. To tackle this complexity, hundreds of dedicated analysis techniques have been conceived, many of which able to analyze system properties for all possible system configurations, as opposed to traditional, single-system analyses. Unfortunately, it is largely unknown whether these techniques are adopted in practice, whether they address actual needs, or what strategies practitioners actually apply to analyze highly configurable systems. We present a study of analysis practices and needs in industry. It relied on a survey with 27 practitioners engineering highly configurable systems and follow-up interviews with 15 of them, covering 18 different companies from eight countries. We confirm that typical properties considered in the literature (e.g., reliability) are relevant, that consistency between variability models and artifacts is critical, but that the majority of analyses for specifications of configuration options (a.k.a., variability model analysis) is not perceived as needed. We identified rather pragmatic analysis strategies, including practices to avoid the need for analysis. For instance, testing with experience-based sampling is the most commonly applied strategy, while systematic sampling is rarely applicable. We discuss analyses that are missing and synthesize our insights into suggestions for future research.

Product Lines (PL) in the systems engineering (SE) domain are one of the largest and most complex ones. The sheer number of different products that can be derived from PL points out to the scale of the challenge that Product Line Engineering (PLE) faces. Various development artifacts describe PL but due to their diversity, variability modeling across PL is a challenging task. Moreover, this complexity is a major obstacle for achieving traceability across PL which is especially important for product verification. In order to support systems engineering by establishing traceability across PL and aid verification planning we propose Multi-View Variability Model (MVVM). MVVM introduces a set of variability models that represent variability in various development artifacts, e.g. architecture, requirements etc. and corresponding inter-model constraints. We provide a formalization of MVVM and perform a transformation of the MVVM model to a Constraint Satisfiability Problem (CSP) where we formulate queries for the CSP model in order to extract information about variability dependencies among MVVM views. Throughout the paper we use a real system from the automotive domain as the working example in order to illustrate the introduced concepts.