Being the most popular programming language for developing Ethereum smart contracts, Solidity allows using inline assembly to gain fine-grained control. Although many empirical studies on smart contracts have been conducted, to the best of our knowledge, none has examined inline assembly in smart contracts. To fill the gap, in this paper, we conduct the first large-scale empirical study of inline assembly on >7.6 million open-source Ethereum smart contracts from three aspects, namely, source code, bytecode, and transactions after designing new approaches to tackle several technical challenges. Through a thorough quantitative and qualitative analysis of the collected data, we obtain many new observations and insights. Moreover, by conducting a questionnaire survey on using inline assembly in smart contracts, we draw new insights from the valuable feedback. This work sheds light on the development of smart contracts as well as the evolution of Solidity and its compilers.

Xiao Peng

China EverBright Bank

Shuwei Song

University of Electronic Science and Technology of China

Xiapu Luo

Hong Kong Polytechnic University

China

Ting Chen

University of Electronic Science and Technology of China

China

Jiachi Chen

Sun Yat-Sen University

Tao Zhang

Macau University of Science and Technology (MUST)

China

Xiaosong Zhang

University of Electronic Science and Technology of China

Learning-based program repair has achieved good results in a recent series of papers. Yet, we observe that the related work fails to repair some bugs because of a lack of knowledge about 1) the application domain of the program being repaired, and 2) the fault type being repaired. In this paper, we solve both problems by changing the learning paradigm from supervised training to self-supervised training in an approach called SelfAPR. First, SelfAPR generates and constructs training samples by perturbing a previous version of the program being repaired, enforcing the neural model to capture project-specific knowledge. This is different from the previous work based on mined past commits. Second, SelfAPR extracts and encodes test execution diagnostics into the input representation, steering the neural model to fix the kind of fault. This is different from the existing studies that only consider static source code as input. We implement SelfAPR and evaluate it in a systematic manner. We train SelfAPR with 850 705 training samples obtained by perturbing 17 open-source projects. We evaluate SelfAPR on 818 bugs from Defects4J, SelfAPR correctly repairs 114 of them, outperforming all the supervised learning repair approaches

He Ye

KTH Royal Institute of Technology

Matias Martinez

Université Polytechnique Hauts-de-France

Xiapu Luo

Hong Kong Polytechnic University

China

Tao Zhang

Macau University of Science and Technology (MUST)

China

Martin Monperrus

KTH Royal Institute of Technology

Sweden

Learning-based program repair has achieved good results in a recent series of papers. Yet, we observe that the related work fails to repair some bugs because of a lack of knowledge about 1) the application domain of the program being repaired, and 2) the fault type being repaired. In this paper, we solve both problems by changing the learning paradigm from supervised training to self-supervised training in an approach called SelfAPR. First, SelfAPR generates and constructs training samples by perturbing a previous version of the program being repaired, enforcing the neural model to capture project-specific knowledge. This is different from the previous work based on mined past commits. Second, SelfAPR extracts and encodes test execution diagnostics into the input representation, steering the neural model to fix the kind of fault. This is different from the existing studies that only consider static source code as input. We implement SelfAPR and evaluate it in a systematic manner. We train SelfAPR with 850 705 training samples obtained by perturbing 17 open-source projects. We evaluate SelfAPR on 818 bugs from Defects4J, SelfAPR correctly repairs 114 of them, outperforming all the supervised learning repair approaches.

He Ye

KTH Royal Institute of Technology

Matias Martinez

Université Polytechnique Hauts-de-France

Xiapu Luo

Hong Kong Polytechnic University

China

Tao Zhang

Macau University of Science and Technology (MUST)

China

Martin Monperrus

KTH Royal Institute of Technology

Sweden