Omar Al-Bataineh

Xiao Liang Yu, Omar Al-Bataineh, David Lo et al.

Smart contracts are automated or self-enforcing contracts that can be used to exchange assets without having to place trust in third parties. Many commercial transactions use smart contracts due to their potential benefits in terms of secure peer-to-peer transactions independent of external parties. Experience shows that many commonly used smart contracts are vulnerable to serious malicious attacks which may enable attackers to steal valuable assets of involving parties. There is therefore a need to apply analysis and automated repair techniques to detect and repair bugs in smart contracts before being deployed. In this work, we present the first general-purpose automated smart contract repair approach that is also gas-aware. Our repair method is search-based and searches among mutations of the buggy contract. Our method also considers the gas usage of the candidate patches by leveraging our novel notion of gas dominance relationship. We have made our smart contract repair tool SCRepair available open-source, for investigation by the wider community.

Omar Al-Bataineh, Daniel Jun Xian Ng, Arvind Easwaran

This paper considers the problem of decentralized monitoring of a class of non-functional properties (NFPs) with quantitative operators, namely cumulative cost properties. The decentralized monitoring of NFPs can be a non-trivial task for several reasons: (i) they are typically expressed at a high abstraction level where inter-event dependencies are hidden, (ii) NFPs are difficult to be monitored in a decentralized way, and (iii) lack of effective decomposition techniques. We address these issues by providing a formal framework for decentralised monitoring of LTL formulas with quantitative operators. The presented framework employs the tableau construction and a formula unwinding technique (i.e., a transformation technique that preserves the semantics of the original formula) to split and distribute the input LTL formula and the corresponding quantitative constraint in a way such that monitoring can be performed in a decentralised manner. The employment of these techniques allows processes to detect early violations of monitored properties and perform some corrective or recovery actions. We demonstrate the effectiveness of the presented framework using a case study based on a Fischertechnik training model,a sorting line which sorts tokens based on their color into storage bins. The analysis of the case study shows the effectiveness of the presented framework not only in early detection of violations, but also in developing failure recovery plans that can help to avoid serious impact of failures on the performance of the system.