# The Blockchain Oracle Problem in Decentralized Finance--A Multivocal Approach
## 3 Methodology
### Results  
As described in the Methodology section, the exclusion criteria did not restrict articles to those published within a specific timeframe, and this resulted in articles retrieved for only the past three years. Figure 3 depicts the results. First, only articles between 2019 and 2021 were retrieved due to the subject's nascency. As shown, there is an increase in 2020, and as expected, the numbers reported for 2021 are lower, since data gathering was finished by March 2021. However, if the trend remains constant, the number of published articles of 2021 may exceed double of 2020. As discussed in the Methodology section, a majority of articles are grey literature, while the others are academic papers drawn from Scopus (4) and Google Scholar (11). Figure 4 displays this result. To provide a better breakdown of the results, an overview of topics was then first distinguished into available (Y) and non-available (N), and according to the source between academic papers (AP) and grey literature (GL). The results are shown in Figure 5. The figure already gives an idea of why the MLR was strictly necessary to address the questions of the paper. While both academic and grey literature discussed risks relating to oracles, mainly grey literature outlined \"real\" incidents with oracles in DeFi. Similarly, while both equally described DeFi applications, mainly grey literature outlined actual DeFi companies and Oracles providers. The paucity of these data in the academic literature gives the idea that the analysis is still at a general level and requires more practical case studies. Furthermore, if not strictly necessary for the empirical analysis, reviewed academic research did not mention real companies (likely due to blind research) and discussed the phenomenon in a broad sense. In other words, it is common to read discussions about Automatic Market Makers and not specific analyses of Uniswap or Curve. In our case, however, it is important to gather data on real projects to understand state-of-the-art with respect to oracle implementation in the DeFi space. For that reason, again, the involvement of grey literature was considered necessary. Finally, we provide a chart in Figure 6, displaying the availability of data for each DeFi sector. The graph displays the number of papers that dedicated a paragraph or a section to a specific application type. Of course, the same paper may have a dedicated paragraph to multiple applications while other may not have dedicated paragraph or sections to a specific application. \begin{table}
\begin{tabular}{c c} \hline \hline
**Data Item** & **Description** \\ \hline Title & Title of the paper \\ \hline Year & Time of Publication \\ \hline Source Type & Academic Article or Grey Literature \\ \hline Source Name & The name of the Site, Journal, or Proceedings \\ \hline DeFi Applications & Discussed DeFi Applications \\ \hline Applications type & Which application was described \\ \hline DeFi Companies & Presented DeFi Projects \\ \hline Risks and Attack vectors & Discussed risks connected with the use of oracles in DeFi \\ \hline Issues with oracles & Discussed real incidents in DeFi connected with the use of oracles. \\ \hline Oracle Providers & Described Oracle providers who operate in the DeFi Domain \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Data-extraction items. Figure 4: Article type/number. Figure 5: Data-acquisition results. Figure 3: Publications/year. From the result, we can observe that academic literature, as well as grey literature, focused mainly on lending and liquidity pools while fewer focused on derivatives. This also reflects the lower availability and early stage of decentralized derivatives services. This, unfortunately, also affects the availability of data on oracles in the derivatives sector. Therefore, while more examples are available in the lending, liquidity pools, and stablecoins field, lesser are provided for derivative ones. Appendix B provides a sample of AP and GL divided by subject to facilitate the reproduction of results.