Blockchain technologies are gradually being found an application in many areas, especially in FinTech. As a result, a lot of blockchain platforms have emerged with the support of smart contracts that are intended to automate party interactions. However, it has been shown that they are prone to attacks and errors which lead to money loss. To date, there has been a wide range of approaches for making smart contracts safer that included analysis tools, reasoning models, and safer and more rigorous programming languages. In this paper, we provide an overview of smart contract programming languages design principles, related vulnerabilities, and future research areas. The provided overview is meant to outline the to date state of languages and to become a possible basis for future proceedings, and show approaches, used by the community, to reach safe and usable language for smart contracts. We have split all found vulnerabilities by source of their arising. Various languages' characteristics such as abstraction level, paradigm, Turing completeness and main features are summarized in the table. Additional information about languages is provided, e.g. model of execution and tools for static analysis.

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Abstract. Many common programming tasks, like connecting to a database, drawing an image, or reading from a file, are long implemented in various frameworks and are available via corresponding Application Programming Interfaces (APIs). However, to use them, a software engineer must first learn of their existence and then of the correct way to utilize them. Currently, the Internet seems to be the best and the most common way to gather such information. Recently, a deep-learning-based solution was proposed in the form of DeepAPI tool. Given English description of the desired functionality, sequence of Java function calls is generated. In this paper, we show the way to apply this approach to a different programming language (C# over Java) that has smaller open code base; we describe techniques used to achieve results close to the original, as well as techniques that failed to produce an impact. Finally, we release our dataset, code and trained model to facilitate further research.

Abstract-String-embedded language transformation is one of the problems which can be faced during database and information system migration. The conventional solution which is provided by a number of tools is based on run-time translation. We present a static abstract translation approach which originates from the abstract parsing technique [9] initially developed for syntax analysis of string-embedded languages. We present abstract translation algorithm and some optimization techniques, and discuss the results of its evaluation on a real-world industrial application.