Optimizing Gas Consumption in Ethereum Smart Contracts: Best Practices and Techniques
Bibliographic record
Abstract
Full-fledged applications, known as “smart contracts,” may be executed on blockchains. At this time, the quantity of Ethereum smart contracts written in the Solidity programming language is skyrocketing. The cost for executing smart contract code is measured using gas. Gas is used to allocate resources of the Ethereum virtual machine (EVM) so that wallet transactions and smart contract transactions can self-execute. Complicated transactions involving smart contracts require more computational work, so they require a higher gas amount than a simple payment. Optimizing smart contract code is an important practice in software engineering smart contracts and that to reduce gas consumption and, in some instances, to even avoid malicious attacks. This means that reducing the cost of gas consumption in smart contracts is important for anyone who use it, including developers. For inexperienced programmers, learning the mechanics of a smart contract and blockchain technology may be a considerable hurdle when it comes to gas optimization. In this paper, we present around 28 gas efficient patterns with examples in solidity, providing data on how much gas each pattern saves. We provide a categorization of those code patterns and a comparison between the state of the art tools used to address the problem of gas optimization in smart contracts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".