Omar I. 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 I. Al-Bataineh, Anastasiia Grishina, Leon Moonen

A long-standing open challenge for automated program repair is the overfitting problem, which is caused by having insufficient or incomplete specifications to validate whether a generated patch is correct or not. Most available repair systems rely on weak specifications (i.e., specifications that are synthesized from test cases) which limits the quality of generated repairs. To strengthen specifications and improve the quality of repairs, we propose to closer integrate static bug detection techniques with automated program repair. The integration combines automated program repair with static analysis techniques in such a way that bug detection patterns can be synthesized into specifications that the repair system can use. We explore the feasibility of such integration using two types of bugs: arithmetic bugs, such as integer overflow, and logical bugs, such as termination bugs. As part of our analysis, we make several observations that help to improve patch generation for these classes of bugs. Moreover, these observations assist with narrowing down the candidate patch search space, and inferring an effective search order.

Umair Z. Ahmed, Zhiyu Fan, Jooyong Yi et al.

Automated feedback generation for introductory programming assignments is useful for programming education. Most works try to generate feedback to correct a student program by comparing its behavior with an instructor's reference program on selected tests. In this work, our aim is to generate verifiably correct program repairs as student feedback. The student assignment is aligned and composed with a reference solution in terms of control flow, and differences in data variables are automatically summarized via predicates to relate the variable names. Failed verification attempts for the equivalence of the two programs are exploited to obtain a collection of maxSMT queries, whose solutions point to repairs of the student assignment. We have conducted experiments on student assignments curated from a widely deployed intelligent tutoring system. Our results indicate that we can generate verified feedback in up to 58% of the assignments. More importantly, our system indicates when it is able to generate a verified feedback, which is then usable by novice students with high confidence.

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.

Omar I. Al-Bataineh, Mark Reynolds

The timed-based automata model, introduced by Alur and Dill, provides a useful formalism for describing real-time systems. Over the last two decades, several dense-time model checking tools have been developed based on that model. The paper considers the verification of real-time distributed commit protocols using dense-time model checking technology. More precisely, we model and verify the well-known timed two phase commit protocol in three different state-of-the-art real-time model checkers: UPPAAL, Rabbit, and RED, and compare the results.