
doi: 10.1109/seaa.2014.15
In mechatronic and embedded systems, variability stretches from customer-visible features to implementation features, which manifest in software, hardware, and mechanical parts. A good example are automotive systems, which are usually implemented as product lines. There are close connections between hardware and software during the development of such product lines. For example, software usually needs to be heavily tuned towards processors characteristics or optimized for a specific memory size. The problem is that different lifecycles of hardware and software make it difficult to maintain all variability in a single model. In this paper, the notion of hardware variability is discussed. We suggest that software and hardware variability should be kept in separate models. We argue that hardware variability and software variability models should only be loosely coupled. This allows an easier exchange of hardware platforms and variants as well as a test during the configuration whether hardware and software fit to each other. To address this, we propose an approach that distinguishes between software and hardware variants by using separate variability models. Therefore, we introduce a hardware variability model, which has a strong focus on the description of hardware properties. Furthermore, we introduce a concept for modeling the dependencies between hardware and software variants to combine them during the configuration.
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 13 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
