Smart contracts are self-executing computer programs deployed on blockchain to enable trustworthy exchange of value without the need of a central authority. With the absence of documentation and specifications, routine tasks such as program understanding, maintenance, verification, and validation, remain challenging for smart contracts. In this paper, we propose a dynamic invariant detection tool, InvCon, for Ethereum smart contracts to mitigate this issue. The detected invariants can be used to not only support the reverse engineering of contract specifications, but also enable standard-compliance checking for contract implementations. InvCon provides a Web-based interface and a demonstration video of it is available at: https://youtu.be/Y1QBHjDSMYk.

Ye Liu

Nanyang Technological University

Yi Li

Nanyang Technological University, Singapore

Singapore

Third-party libraries (TPLs) are frequently reused in software to reduce development cost and the time-to-market. However, exter- nal library dependencies may introduce vulnerabilities into host applications. The issue of library dependency has received consid- erable critical attention. Many package managers like Maven, Pip, NPM are proposed to manage TPLs, and there has been a lot of effort put into studying dependencies in language ecosystem like Java, Python, JavaScript except C/C++. Due to the lack of a unified package manager for C/C++, existing research has only a few un- derstanding of TPL dependency in C/C++ ecosystem, especially at large-scale.

Towards Understanding TPL dependencies in C/C++ ecosystem, we collect existing TPL databases, package management tools and dependency detection tools, summarize the dependency patterns of C/C++ projects, and construct a comprehensive and precise C/C++ dependency detector. Using our detector, we extract dependencies from a large-scale database containing 24K C/C++ repositories from GitHub. Based on the extracted dependencies, we provide the results and findings of an empirical study aimed at understanding the characteristics of the TPL dependencies. We further discuss the challenges to manage dependency for C/C++ and the future directions for software engineering researchers and developers in fields of software composition analysis, C/C++ package manager and library development. Our dataset of extracted dependencies used in this work are anonymously available at url:

Wei Tang

Tsinghua University

Zhengzi Xu

Nanyang Technological University

Chengwei Liu

Nanyang Technological University, Singapore

Wu Jiahui

Nanyang Technological University

shouguo yang

Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China

Yi Li

Nanyang Technological University, Singapore

Singapore

Ping Luo

Tsinghua University

Yang Liu

Nanyang Technological University

Smart contracts are gaining popularity as a means to support transparent, traceable, and self-executing decentralized applications, which enable the exchange of value in a trustless environment. Developers of smart contracts rely on various libraries, such as OpenZeppelin for Solidity contracts, to improve application quality and reduce development costs. The API documentations of these libraries are important sources of information for developers who are unfamiliar with the APIs. Yet, maintaining high-quality documentations is non-trivial, and errors in documentations may place barriers for developers to learn the correct usages of APIs. In this paper, we propose a technique, DocCon, to detect inconsistencies between documentations and the corresponding code for Solidity smart contract libraries. Our fact-based approach allows inconsistencies of different severity levels to be queried, from a database containing precomputed facts about the API code and documentations. DocCon successfully detected high-priority API documentation errors in popular smart contract libraries, including mismatching parameters, missing requirements, outdated descriptions, etc. Our experiment result shows that DocCon achieves good precision and is applicable to different libraries: 29 and 22 out of our reported 40 errors have been confirmed and fixed by library developers so far.

Chenguang Zhu

The University of Texas at Austin

United States

Ye Liu

Nanyang Technological University

Xiuheng Wu

Nanyang Technological University, Singapore

Singapore

Yi Li

Nanyang Technological University, Singapore

Singapore

Programming errors enable security attacks on smart contracts, which are used to manage large sums of financial assets. Automated program repair (APR) techniques aim to reduce developers’ burden of manually fixing bugs by automatically generating patches for a given issue. Existing APR tools for smart contracts focus on mitigating typical smart contract vulnerabilities rather than violations of functional specification. However, in decentralized financial (DeFi) smart contracts, the inconsistency between intended behavior and implementation translates into the deviation from the underlying financial model, resulting in irrecoverable monetary losses for the application and its users. In this work, we propose DeFinery—a technique for automated repair of a smart contract that does not satisfy a user-defined correctness property, financial or otherwise. To explore a larger set of diverse patches while providing formal correctness guarantees w.r.t. the intended behavior, we combine search-based patch generation with semantic analysis of an original program for inferring its specification. Our experiments in repairing nine real-world and benchmark smart contracts reveal that DeFinery efficiently navigates the search space and generates higher-quality patches that cannot be obtained by other smart contract APR tools.

Palina Tolmach

Nanyang Technological University, Singapore, Institute of High Performance Computing, Agency for Science, Technology and Research (A*STAR), Singapore

Yi Li

Nanyang Technological University, Singapore

Singapore

Shang-Wei Lin

Nanyang Technological University

Singapore

Smart contracts are gaining popularity as a means to support transparent, traceable, and self-executing decentralized applications, which enable the exchange of value in a trustless environment. Developers of smart contracts rely on various libraries, such as OpenZeppelin for Solidity contracts, to improve application quality and reduce development costs. The API documentations of these libraries are important sources of information for developers who are unfamiliar with the APIs. Yet, maintaining high-quality documentations is non-trivial, and errors in documentations may place barriers for developers to learn the correct usages of APIs. In this paper, we propose a technique, DocCon, to detect inconsistencies between documentations and the corresponding code for Solidity smart contract libraries. Our fact-based approach allows inconsistencies of different severity levels to be queried, from a database containing precomputed facts about the API code and documentations. DocCon successfully detected high-priority API documentation errors in popular smart contract libraries, including mismatching parameters, missing requirements, outdated descriptions, etc. Our experiment result shows that DocCon achieves good precision and is applicable to different libraries: 29 and 22 out of our reported 40 errors have been confirmed and fixed by library developers so far.

Chenguang Zhu

The University of Texas at Austin

United States

Ye Liu

Nanyang Technological University

Xiuheng Wu

Nanyang Technological University, Singapore

Singapore

Yi Li

Nanyang Technological University, Singapore

Singapore

Programming errors enable security attacks on smart contracts, which are used to manage large sums of financial assets. Automated program repair (APR) techniques aim to reduce developers’ burden of manually fixing bugs by automatically generating patches for a given issue. Existing APR tools for smart contracts focus on mitigating typical smart contract vulnerabilities rather than violations of functional specification. However, in decentralized financial (DeFi) smart contracts, the inconsistency between intended behavior and implementation translates into the deviation from the underlying financial model, resulting in irrecoverable monetary losses for the application and its users. In this work, we propose DeFinery—a technique for automated repair of a smart contract that does not satisfy a user-defined correctness property, financial or otherwise. To explore a larger set of diverse patches while providing formal correctness guarantees w.r.t. the intended behavior, we combine search-based patch generation with semantic analysis of an original program for inferring its specification. Our experiments in repairing nine real-world and benchmark smart contracts reveal that DeFinery efficiently navigates the search space and generates higher-quality patches that cannot be obtained by other smart contract APR tools.

Palina Tolmach

Nanyang Technological University, Singapore, Institute of High Performance Computing, Agency for Science, Technology and Research (A*STAR), Singapore

Yi Li

Nanyang Technological University, Singapore

Singapore

Shang-Wei Lin

Nanyang Technological University

Singapore

Abstract—Over the past few years, SMT string solvers have found their applications in an increasing number of domains, such as program analyses in mobile and Web applications, which require the ability to reason about string values. A series of research has been carried out to find quality issues of string solvers in terms of its correctness and performance. Yet, none of them has considered the performance regressions happening across multiple versions of a string solver. To fill this gap, in this paper, we focus on solver performance regressions (SPRs), i.e., unintended slowdowns introduced during the evolution of string solvers. To this end, we develop SPRFinder to not only generate test cases demonstrating SPRs, but also localize the probable causes of them, in terms of commits. We evaluated the effectiveness of SPRFinder on three state-of-the-art string solvers, i.e., Z3Seq, Z3Str3, and CVC4. The results demonstrate that SPRFinder is effective in generating SPR-inducing test cases and also able to accurately locate the responsible commits. Specifically, the average running time on the target versions is 13.2× slower than that of the reference versions. Besides, we also conducted the first empirical study to peek into the characteristics of SPRs, including the impact of random seed configuration for SPR detection, understanding the root causes of SPRs, and characterizing the regression test cases through case studies. Finally, we highlight that 149 unique SPR-inducing commits were discovered in total by SPRFinder, and 16 of them have been confirmed by the corresponding developers. The original paper can be accessed from https://ieeexplore.ieee.org/abstract/document/9760153

Yao Zhang

Xiaofei Xie

Singapore Management University, Singapore

Singapore

Yi Li

Nanyang Technological University, Singapore

Singapore

Yun Lin

National University of Singapore

Singapore

Sen Chen

Tianjin University

China

Yang Liu

Nanyang Technological University

Xiaohong Li

TianJin University

China