You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
Abstraction is one of the fundamental concepts of software design. Consequently, the determination of an appropriate abstraction level for the multitude of artefacts that form a software system is an integral part of software engineering. However, the very nature of abstraction in software design and particularly its interrelation with equally important concepts like complexity, specificity or genericity are not fully understood today. As a step towards a better understanding of the trade-offs involved, this paper proposes a distinction of abstraction into two types that have different effects on the specificity and the complexity of artefacts. We discuss the roles of the two types of abstraction in software design and explain the interrelations between abstractness, specificity, and complexity. Furthermore, we illustrate the benefit of the proposed distinction with multiple examples and describe consequences of our findings for software design activities.

Cited Abstract(s):
[1]: The complexity of modern scientific simulations combined with the complexity of the high-performance computer hardware on which they run places an ever-increasing burden on scientific software developers, with clear impacts on both productivity and performance. We argue that raising the level of abstraction of the programming model environment is a key element of addressing this situation. We present examples of two distinctly different approaches to raising the level of abstraction of the programming model while maintaining or increasing performance: the Tensor Contraction engine, a narrowly-focused domain specific language together with an optimizing compiler; and Extended Global Arrays, a programming framework that integrates programming models dealing with different layers of the memory storage hierarchy using compiler analysis and code transformation techniques.
Finally, the paper of Bernholdt, Nieplocha, and Sadayappan [1] is an example that neglects the tradeoffs discussed in this paper. They classify languages for high-performance computing in the dimensions abstraction'' and generality''. Based on this it is stated that the ultimate goal of further language developments should be to rank high in both dimensions, i.e., that new languages should be more abstract and more general at the same time. We show that this is only comes at the cost of higher dynamic complexity although their first goal was complexity reduction.