The development of variable software, in general, and feature models, in particular, is an error-prone and time-consuming task. It gets increasingly more challenging with industrial-size models containing hundreds or thousands of features and constraints. Each change may lead to anomalies in the feature model such as making some features impossible to select. While the detection of anomalies is well-researched, giving explanations is still a challenge. Explanations must be as accurate and understandable as possible to support the developer in repairing the source of an error. We propose an efficient and generic algorithm for explaining different anomalies in feature models. Additionally, we achieve a benefit for the developer by computing short explanations expressed in a user-friendly manner and by emphasizing specific parts in explanations that are more likely to be the cause of an anomaly. We provide an open-source implementation in FeatureIDE and show its scalability for industrial-size feature models.

Automated machines and plants are operated for some decades and undergo an everlasting evolution during this time. In this paper, we present three related open evolution challenges focusing on software evolution in the domain of automated production systems, i.e. evolution and co-evolution of (interdisciplinary) engineering models and code, quality assurance as well as variant and version management during evolution.

Automated production systems involve multiple engineering disciplines and often operate for several decades. Therefore, in order to leverage benefits of model-based system engineering, modeling approaches must handle both multiple disciplines and variability. As a first step towards variability modeling and management, a small case study is carried out to investigate the opportunities and challenges when enhancing an interdisciplinary modeling approach with delta modeling - a technique for specifying variants and versions. The paper reflects critically its applicability, implications as well as potential beneficial impacts and discusses encouraging challenges and next steps towards an interdisciplinary approach for variability modeling and management.

In software performance engineering, what-if scenarios, architecture optimization, capacity planning, run-time adaptation, and uncertainty management of realistic models typically require the evaluation of many instances. Effective analysis is however hindered by two orthogonal sources of complexity. The first is the infamous problem of state space explosion -- the analysis of a single model becomes intractable with its size. The second is due to massive parameter spaces to be explored, but such that computations cannot be reused across model instances. In this paper, we efficiently analyze many queuing models with the distinctive feature of more accurately capturing variability and uncertainty of execution rates by incorporating general (i.e., non-exponential) distributions. Applying product-line engineering methods, we consider a family of models generated by a core that evolves into concrete instances by applying simple delta operations affecting both the topology and the model's parameters. State explosion is tackled by turning to a scalable approximation based on ordinary differential equations. The entire model space is analyzed in a family-based fashion, i.e., at once using an efficient symbolic solution of a super-model that subsumes every concrete instance. Extensive numerical tests show that this is orders of magnitude faster than a naive instance-by-instance analysis.