We present a measurement study on compositions of Decentralized Finance (DeFi) protocols, which aim to disrupt traditional finance and offer services on top of distributed ledgers, such as Ethereum. Understanding DeFi compositions is of great importance, as they may impact the development of ecosystem interoperability, are increasingly integrated with web technologies, and may introduce risks through complexity. Starting from a dataset of 23 labeled DeFi protocols and 10,663,881 associated Ethereum accounts, we study the interactions of protocols and associated smart contracts. From a network perspective, we find that decentralized exchange (DEX) and lending protocol account nodes have high degree and centrality values, that interactions among protocol nodes primarily occur in a strongly connected component, and that known community detection methods cannot disentangle DeFi protocols. Therefore, we propose an algorithm to decompose a protocol call into a nested set of building blocks that may be part of other DeFi protocols. This allows us to untangle and study protocol compositions. With a ground truth dataset that we have collected, we can demonstrate the algorithm’s capability by finding that swaps are the most frequently used building blocks. As building blocks can be nested, that is, contained in each other, we provide visualizations of composition trees for deeper inspections. We also present a broad picture of DeFi compositions by extracting and flattening the entire nested building block structure across multiple DeFi protocols. Finally, to demonstrate the practicality of our approach, we present a case study that is inspired by the recent collapse of the UST stablecoin in the Terra ecosystem. Under the hypothetical assumption that the stablecoin USD Tether would experience a similar fate, we study which building blocks — and, thereby, DeFi protocols — would be affected. Overall, our results and methods contribute to a better understanding of a new family of financial products.

The analysis of power cycling in semiconductor modules is often based on a normalized lifetime consumption or cumulative damage models added up via Miner's rule. Although this is a widely accepted method to estimate a predicted lifetime it is not possible to overlay those results with other wear-out mechanisms caused by aging degradation progress in time. The paper describes a method to derive a reasonable aging model based on typical power cycling data provided by industry.