Context: Superoptimization is a synthesis technique that, given a loop-free sequence of instructions, searches for an equivalent sequence that is optimal wrt. an objective function. Superoptimization of Ethereum smart contracts aims at minimizing the size of their bytecode and the gas consumption of executing the contract’s functions. The search for the optimal solution poses huge computational demands –as the search space to find the optimal sequence is exponential on the given size-bound – being the main challenge for superoptimization today to scale up to real, industrial software. Even if the underlying problem for finding the optimal solution is decidable, practical tools often prioritize efficiency over completeness. This means they might be implemented to find a sub-optimal solution or even time out. Objective: This work aims at leveraging superoptimization to a real setting: Ethereum blockchain. This paper proposes a neural-guided superoptimization (NGS) approach which incorporates deep neural networks using (supervised) learning into superoptimization to improve scalability by predicting: (1) if a sequence is already optimal and hence the search can be skipped; (2) the size-bound for the optimal solution in order to reduce the search space. Method: We have downloaded over 13,000 smart contracts deployed on the blockchain for training and testing the machine learning models, and a disjoint set with 100 of the smart contracts with more transactions to prove our scalability gains and impact for the Ethereum community. Results: Incorporating DNNs resulted in a 16x overall speedup (12x for gas) with only 12% optimization loss (14% for gas), or a 3-4x speedup with no optimization loss. For the 100 analyzed contracts, this approach reduced the average compilation time to 3 min per contract and achieved monetary savings of $1.24M. Conclusions: The integration of machine learning models mitigates several limitations of traditional superoptimization by drastically reducing execution times while maintaining most of the original optimization gains.

A storage is a persistent memory whose contents are kept across different program executions. In the blockchain technology, storage contents are replicated and incur the largest costs of a program’s execution (a.k.a. gas fees). Storage costs are dynamically calculated using a rather complex model which assigns a much larger cost to the first access made in an execution to a storage key, and besides assigns different costs to write accesses depending on whether they change the values w.r.t. the initial and previous contents. Safely assuming the largest cost for all situations, as done in existing gas analyzers, is an overly-pessimistic approach that might render useless bounds because of being too loose. The challenge is to soundly, and yet accurately, synthesize storage bounds which take into account the dynamicity implicit to the cost model. Our solution consists in using an off-the-shelf static resource analysis —but do not always assuming a worst-case cost— and hence yielding unsound bounds; and then, in a posterior stage, computing corrections to recover soundness in the bounds by using a new Max-SMT based approach. We have implemented our approach and used it to improve the precision of two gas analyzers for Ethereum, gastap and asparagus. Experimental results on more than 400,000 functions show that we achieve great accuracy gains, up to 75%, on the storage bounds, being the most frequent gains between 10-20%.

Abstract Efficiency is a fundamental property of any type of program, but it is even more so in the context of the programs executing on the blockchain (known as smart contracts ). This is because optimizing smart contracts has direct consequences on reducing the costs of deploying and executing the contracts, as there are fees to pay related to their bytes-size and to their resource consumption (called gas ). Optimizing memory usage is considered a challenging problem that, among other things, requires a precise inference of the memory locations being accessed. This is also the case for the Ethereum Virtual Machine (EVM) bytecode generated by the most-widely used compiler, , whose rather unconventional and low-level memory usage challenges automated reasoning. This paper presents a static analysis, developed at the level of the EVM bytecode generated by , that infers write memory accesses that are needless and thus can be safely removed. The application of our implementation on more than 19,000 real smart contracts has detected about 6,200 needless write accesses in less than 4 hours. Interestingly, many of these writes were involved in memory usage patterns generated by that can be greatly optimized by removing entire blocks of bytecodes. To the best of our knowledge, existing optimization tools cannot infer such needless write accesses, and hence cannot detect these inefficiencies that affect both the deployment and the execution costs of Ethereum smart contracts.

Abstract Superoptimization is a compilation technique that searches for the optimal sequence of instructions semantically equivalent to a given (loop-free) initial sequence. With the advent of SMT solvers, it has been successfully applied to LLVM code (to reduce the number of instructions) and to Ethereum EVM bytecode (to reduce its gas consumption). Both applications, when proven practical, have left out memory operations and thus missed important optimization opportunities. A main challenge to superoptimization today is handling memory operations while remaining scalable. We present $$\textsf {GASOL}^{v2}$$ GASOL v 2 , a gas and bytes-size superoptimization tool for Ethereum smart contracts, that leverages a previous Max-SMT approach for only stack optimization to optimize also wrt. memory and storage. $$\textsf {GASOL}^{v2}$$ GASOL v 2 can be used to optimize the size in bytes, aligned with the optimization criterion used by the Solidity compiler , and it can also be used to optimize gas consumption. Our experiments on 12,378 blocks from 30 randomly selected real contracts achieve gains of 16.42% in gas wrt. the previous version of the optimizer without memory handling, and gains of 3.28% in bytes-size over code already optimized by .

Smart contracts are programs deployed on a blockchain. They are executed for a monetary fee paid in gas —a clear optimization target for smart contract compilers. Because smart contracts are a young, fast-moving field without (manually) fine-tuned compilers, they highly benefit from automated and adaptable approaches, especially as smart contracts are effectively immutable, and as such need a high level of assurance. This makes them an ideal domain for applying formal methods. Super-optimization is a technique to find the best translation of a block of instructions by trying all possible sequences of instructions that produce the same result. We present a framework for super-optimizing smart contracts based on Max-SMT with two main ingredients: (1) a stack functional specification extracted from the basic blocks of a smart contract, which is simplified using rules capturing the semantics of arithmetic, bit-wise, and relational operations, and (2) the synthesis of optimized blocks , which finds—by means of an efficient SMT encoding—basic blocks with minimal gas cost whose stack functional specification is equal (modulo commutativity) to the extracted one. We implemented our framework in the tool syrup 2.0 . Through large-scale experiments on real-world smart contracts, we analyze performance improvements for different SMT encodings, as well as tradeoffs between quality of optimizations and required optimization time.

With the advent of smart contracts that execute on the blockchain ecosystem, a new mode of reasoning is required for developers that must pay meticulous attention to the gas spent by their smart contracts, as well as for optimization tools that must be capable of effectively reducing the gas required by the smart contracts. Super-optimization is a technique which attempts to find the best translation of a block of code by trying all possible sequences of instructions that produce the same result. This paper presents a novel approach for super-optimization of smart contracts based on Max-SMT which is split into two main phases: (i) the extraction of a stack functional specification from the basic blocks of the smart contract, which is simplified using rules that capture the semantics of the arithmetic, bit-wise, relational operations, etc. (ii) the synthesis of optimized blocks which, by means of an efficient Max-SMT encoding, finds the bytecode blocks with minimal gas cost whose stack functional specification is equal (modulo commutativity) to the extracted one. Our experimental results are very promising: we are able to optimize 55.41 % of the blocks, and prove that 34.28 % were already optimal, for more than 61000 blocks from the most called 2500 Ethereum contracts.

A smart contract is a software program that runs on top of a blockchain. It contains a collection of public functions that can be invoked within the transactions launched over the contract by parties interacting with it. Being computer programs, well-studied formal verification techniques can be applied to them. Indeed, smart contracts are a very interesting application domain for validation, verification and optimization techniques since (1) they are relatively small in size, hence the application of these techniques scales better than when applied to larger industrial code, (2) they are valuable (in the corresponding blockchain cryptocurrency), hence software bugs or inefficiencies can cause economical losses and there is much interest in formally proving their safety and security, and (3) they require proving new specific properties to ensure their reliability and efficiency.

Abstract We present the main concepts, components, and usage of G asol , a Gas AnalysiS and Optimization tooL for Ethereum smart contracts. G asol offers a wide variety of cost models that allow inferring the gas consumption associated to selected types of EVM instructions and/or inferring the number of times that such types of bytecode instructions are executed. Among others, we have cost models to measure only storage opcodes, to measure a selected family of gas-consumption opcodes following the Ethereum’s classification, to estimate the cost of a selected program line, etc. After choosing the desired cost model and the function of interest, G asol returns to the user an upper bound of the cost for this function. As the gas consumption is often dominated by the instructions that access the storage, G asol uses the gas analysis to detect under-optimized storage patterns, and includes an (optional) automatic optimization of the selected function. Our tool can be used within an Eclipse plugin for which displays the gas and instructions bounds and, when applicable, the gas-optimized function.

Ethereum smart contracts are public, immutable and distributed and, as such, they are prone to vulnerabilities sourcing from programming mistakes of developers. This paper presents SAFEVM, a verification tool for Ethereum smart contracts that makes use of state-of-the-art verification engines for C programs. SAFEVM takes as input an Ethereum smart contract (provided either in Solidity source code, or in compiled EVM bytecode), optionally with assert and require verification annotations, and produces in the output a report with the verification results. Besides general safety annotations, SAFEVM handles the verification of array accesses: it automatically generates SV-COMP verification assertions such that C verification engines can prove safety of array accesses. Our experimental evaluation has been undertaken on all contracts pulled from etherscan.io (more than 24,000) by using as back-end verifiers CPAchecker, SeaHorn and VeryMax.