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How to develop secure and optimized blockchain smart contracts? – 5 rules | Nextrope Academy

Paulina Lewandowska

10 Oct 2022
How to develop secure and optimized blockchain smart contracts? – 5 rules | Nextrope Academy
Table of contents

Why is the security of smart contracts important?

Smart contracts are a major part of applications based on blockchain technology. In the development process of smart contracts, we should maintain the highest security standards because of factors such as:

  • in many systems, they are responsible for the most critical functionality, the incorrect operation of which can be associated with a number of very unpleasant consequences, including irreversible loss of funds, a logical error ruining the operation of the entire application/protocol,
  • a smart contract that has already been published on the web cannot be modified. This feature means that bugs and vulnerabilities that are diagnosed after the contract is launched productionally cannot be fixed. (There is an advanced technique to create "upgradeable contracts," which allows the contract logic to be modified later, but it also has a number of other drawbacks and limitations that do not relieve the developer from writing secure code. For the purposes of this article, we will skip a detailed analysis of this solution).
  • The source code of most contracts is publicly available. It is good practice to publish the source code in services such as Etherscan which significantly increases the credibility of the application data or defi protocols. However, making the code publicly available entails that anyone can verify such code for security, and use any irregularities to their advantage.

Learning to write secure smart contracts is a process that requires learning many advanced aspects of the Solidity language. In this article, we will present 5 tips to simplify this process and secure our software from the most common mistakes.

1. Accurate testing of smart contracts

The first, and at the same time the most important factor that allows us to verify that our contract works properly is writing automated tests. The testing process usually allows us to reveal various security gaps or irregularities at an early stage of development. Another advantage of automated tests is protection against code regression, i.e. a situation when during implementation of new functionalities bugs are created in previously written code. In such tests we should check all possible scenarios, 100% code coverage with tests should not be a goal in itself, but only a measure to help us make sure that tests scrupulously check every method on our contract.

2. Configuration of additional tools

It is worthwhile to make use of tools that are able to measure and check the quality of the software we provide. Tools you should use in your daily work are:

  • A plugin for measuring code coverage e.g. solidity-coverage. Expanding on the thought from the first point that code coverage should not be an end in itself, it is nevertheless worth having such analytics in the testing process. By analyzing code coverage with tests, we are able to easily see which code fragments require us to write additional tests.
  • Framework for static code analysis e.g. slither, mythril. These are tools that, with the help of static analysis, are able not only to point out places in our code where a vulnerability exists, but also to offer a number of tips. Following these tips can improve not only the security, but also the quality of our software.

3. Openzeppelin smart contract library

There are many libraries and ready-made contracts that have been prepared for later use by developers of blockchain applications. However, each of these libraries needs to be verified before use to see if it has any vulnerabilities. The most popular library at the moment is openzeppelin. It is a collection of secure, tested smart contracts used in many of DeFi's most popular protocols such as uniswap. It allows us to use the most commonly used implementations of ERC (Ethereum Request For Comments) standards and reusable contracts.

The library has a large range of components that can be used to implement the most popular functionalities on the smart contract side. I will give two applications of the library as examples. However, we believe it is worth exploring all the capabilities and contracts that are provided there.

  • Ownable and AccessControl extensions

These extensions allow us to very easily add access control to functions that, according to business requirements, should only be available for execution to authorized addresses. An example from the documentation showing the use of the Ownable extension in practice:

pragma solidity ^0.8.0;
 
import "@openzeppelin/contracts/access/Ownable.sol";
 
contract MyContract is Ownable {
    function normalThing() public {
        // anyone can call this normalThing()
    }
 
    function specialThing() public onlyOwner {
        // only the owner can call specialThing()!
    }
}

As you can see, using the openzeppelin library is not only very easy, but also allows you to write more concise code that other developers can understand.

  • Implementations of the popular token standards ERC-20, ERC-721 and ERC-1155

Many decentralized applications and protocols are based on ERC-20 or NFT tokens. Each token must have an implemented interface that works according to the specification. Implementing a token entirely on your own is associated with a high risk of error, so our token may have security holes or problems with operation on various exchanges and wallets. With the help of openzeppelin library we are able to prepare a standard, functional token and enrich it with the most popular extensions with little effort. A good place to start is the interactive token configurator in the openzeppelin documentation, it allows us to generate token source code that will meet functional requirements and security standards.

4. Using new versions of the Solidity language

An important safety tip is that projects should use new versions of the Solidity language. The compiler requires us to include Solidity version information at the beginning of each source file with a .sol extension:

pragma solidity 0.8.17;

Along with new versions of the language, new features are introduced, but in addition to this, it is also important that fixes are added to various kinds of known bugs. A list of the bugs found in each version can be found in this file. As you can see, with newer versions of the language the number of bugs decreases and is successively fixed.

The language's developers in the official documentation also recommend using the latest version in newly implemented smart contracts:

“When deploying contracts, you should use the latest released version of Solidity. Apart from exceptional cases, only the latest version receives security fixes”.

5. Learning from other people's mistakes

An essential factor for delivering secure software is the sheer knowledge of the advanced aspects of the Solidity language, as well as awareness of potential threats. In the past, we have witnessed many vulnerabilities where multi-million dollar assets fell prey to the attacker. Many examples of such incidents can be found on the Internet, along with detailed information on what mistake was made by the developers and how it could have been prevented. An example of the above is an article explaining the "reentrancy" attack, with the help of which the attacker stole $150 million worth of ETH. The list of possibilities for attacking smart contracts is definitely longer, so it is worth reading the list of the most popular vulnerabilities in Solidity. A good way to learn security is also to take on the role of an attacker, for this purpose the Ethernaut service is worth a look. There you will find a collection of tasks involving hacking various smart contracts, these tasks will help consolidate previously acquired security knowledge and learn new advanced aspects of the Solidity language.

Summary

In conclusion, software security of decentralized applications is a very important, but also difficult issue requiring knowledge of not only the programming language itself. Also required are testing skills, a willingness to constantly explore the topic of smart contract vulnerabilities, knowledge of new libraries and tools. This topic is vast and complicated and the above 5 points are just guidelines that can help improve the security of our code and with the associated learning. Also take a look at other articles in the Nextrope Academy series, where we take a closer look at other technical issues.

Tagi

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Applying Game Theory in Token Design

Kajetan Olas

16 Apr 2024
Applying Game Theory in Token Design

Blockchain technology allows for aligning incentives among network participants by rewarding desired behaviors with tokens.
But there is more to it than simply fostering cooperation. Game theory allows for designing incentive-machines that can't be turned-off and resemble artificial life.

Emergent Optimization

Game theory provides a robust framework for analyzing strategic interactions with mathematical models, which is particularly useful in blockchain environments where multiple stakeholders interact within a set of predefined rules. By applying this framework to token systems, developers can design systems that influence the emergent behaviors of network participants. This ensures the stability and effectiveness of the ecosystem.

Bonding Curves

Bonding curves are tool used in token design to manage the relationship between price and token supply predictably. Essentially, a bonding curve is a mathematical curve that defines the price of a token based on its supply. The more tokens that are bought, the higher the price climbs, and vice versa. This model incentivizes early adoption and can help stabilize a token’s economy over time.

For example, a bonding curve could be designed to slow down price increases after certain milestones are reached, thus preventing speculative bubbles and encouraging steadier, more organic growth.

The Case of Bitcoin

Bitcoin’s design incorporates game theory, most notably through its consensus mechanism of proof-of-work (PoW). Its reward function optimizes for security (hashrate) by optimizing for maximum electricity usage. Therefore, optimizing for its legitimate goal of being secure also inadvertently optimizes for corrupting natural environment. Another emergent outcome of PoW is the creation of mining pools, that increase centralization.

The Paperclip Maximizer and the dangers of blockchain economy

What’s the connection between AI from the story and decentralized economies? Blockchain-based incentive systems also can’t be turned off. This means that if we design an incentive system that optimizes towards a wrong objective, we might be unable to change it. Bitcoin critics argue that the PoW consensus mechanism optimizes toward destroying planet Earth.

Layer 2 Solutions

Layer 2 solutions are built on the understanding that the security provided by this core kernel of certainty can be used as an anchor. This anchor then supports additional economic mechanisms that operate off the blockchain, extending the utility of public blockchains like Ethereum. These mechanisms include state channels, sidechains, or plasma, each offering a way to conduct transactions off-chain while still being able to refer back to the anchored security of the main chain if necessary.

Conceptual Example of State Channels

State channels allow participants to perform numerous transactions off-chain, with the blockchain serving as a backstop in case of disputes or malfeasance.

Consider two players, Alice and Bob, who want to play a game of tic-tac-toe with stakes in Ethereum. The naive approach would be to interact directly with a smart contract for every move, which would be slow and costly. Instead, they can use a state channel for their game.

  1. Opening the Channel: They start by deploying a "Judge" smart contract on Ethereum, which holds the 1 ETH wager. The contract knows the rules of the game and the identities of the players.
  2. Playing the Game: Alice and Bob play the game off-chain by signing each move as transactions, which are exchanged directly between them but not broadcast to the blockchain. Each transaction includes a nonce to ensure moves are kept in order.
  3. Closing the Channel: When the game ends, the final state (i.e., the sequence of moves) is sent to the Judge contract, which pays out the wager to the winner after confirming both parties agree on the outcome.

A threat stronger than the execution

If Bob tries to cheat by submitting an old state where he was winning, Alice can challenge this during a dispute period by submitting a newer signed state. The Judge contract can verify the authenticity and order of these states due to the nonces, ensuring the integrity of the game. Thus, the mere threat of execution (submitting the state to the blockchain and having the fraud exposed) secures the off-chain interactions.

Game Theory in Practice

Understanding the application of game theory within blockchain and token ecosystems requires a structured approach to analyzing how stakeholders interact, defining possible actions they can take, and understanding the causal relationships within the system. This structured analysis helps in creating effective strategies that ensure the system operates as intended.

Stakeholder Analysis

Identifying Stakeholders

The first step in applying game theory effectively is identifying all relevant stakeholders within the ecosystem. This includes direct participants such as users, miners, and developers but also external entities like regulators, potential attackers, and partner organizations. Understanding who the stakeholders are and what their interests and capabilities are is crucial for predicting how they might interact within the system.

Stakeholders in blockchain development for systems engineering

Assessing Incentives and Capabilities

Each stakeholder has different motivations and resources at their disposal. For instance, miners are motivated by block rewards and transaction fees, while users seek fast, secure, and cheap transactions. Clearly defining these incentives helps in predicting how changes to the system’s rules and parameters might influence their behaviors.

Defining Action Space

Possible Actions

The action space encompasses all possible decisions or strategies stakeholders can employ in response to the ecosystem's dynamics. For example, a miner might choose to increase computational power, a user might decide to hold or sell tokens, and a developer might propose changes to the protocol.

Artonomus, Github

Constraints and Opportunities

Understanding the constraints (such as economic costs, technological limitations, and regulatory frameworks) and opportunities (such as new technological advancements or changes in market demand) within which these actions take place is vital. This helps in modeling potential strategies stakeholders might adopt.

Artonomus, Github

Causal Relationships Diagram

Mapping Interactions

Creating a diagram that represents the causal relationships between different actions and outcomes within the ecosystem can illuminate how complex interactions unfold. This diagram helps in identifying which variables influence others and how they do so, making it easier to predict the outcomes of certain actions.

Artonomus, Github

Analyzing Impact

By examining the causal relationships, developers and system designers can identify critical leverage points where small changes could have significant impacts. This analysis is crucial for enhancing system stability and ensuring its efficiency.

Feedback Loops

Understanding feedback loops within a blockchain ecosystem is critical as they can significantly amplify or mitigate the effects of changes within the system. These loops can reinforce or counteract trends, leading to rapid growth or decline.

Reinforcing Loops

Reinforcing loops are feedback mechanisms that amplify the effects of a trend or action. For example, increased adoption of a blockchain platform can lead to more developers creating applications on it, which in turn leads to further adoption. This positive feedback loop can drive rapid growth and success.

Death Spiral

Conversely, a death spiral is a type of reinforcing loop that leads to negative outcomes. An example might be the increasing cost of transaction fees leading to decreased usage of the blockchain, which reduces the incentive for miners to secure the network, further decreasing system performance and user adoption. Identifying potential death spirals early is crucial for maintaining the ecosystem's health.

The Death Spiral: How Terra's Algorithmic Stablecoin Came Crashing Down
the-death-spiral-how-terras-algorithmic-stablecoin-came-crashing-down/, Forbes

Conclusion

The fundamental advantage of token-based systems is being able to reward desired behavior. To capitalize on that possibility, token engineers put careful attention into optimization and designing incentives for long-term growth.

FAQ

  1. What does game theory contribute to blockchain token design?
    • Game theory optimizes blockchain ecosystems by structuring incentives that reward desired behavior.
  2. How do bonding curves apply game theory to improve token economics?
    • Bonding curves set token pricing that adjusts with supply changes, strategically incentivizing early purchases and penalizing speculation.
  3. What benefits do Layer 2 solutions provide in the context of game theory?
    • Layer 2 solutions leverage game theory, by creating systems where the threat of reporting fraudulent behavior ensures honest participation.

Tagi

Token Engineering Process

Kajetan Olas

13 Apr 2024
Token Engineering Process

Token Engineering is an emerging field that addresses the systematic design and engineering of blockchain-based tokens. It applies rigorous mathematical methods from the Complex Systems Engineering discipline to tokenomics design.

In this article, we will walk through the Token Engineering Process and break it down into three key stages. Discovery Phase, Design Phase, and Deployment Phase.

Table of Contents

Discovery Phase of Token Engineering Process

The first stage of the token engineering process is the Discovery Phase. It focuses on constructing high-level business plans, defining objectives, and identifying problems to be solved. That phase is also the time when token engineers first define key stakeholders in the project.

Defining the Problem

This may seem counterintuitive. Why would we start with the problem when designing tokenomics? Shouldn’t we start with more down-to-earth matters like token supply? The answer is No. Tokens are a medium for creating and exchanging value within a project’s ecosystem. Since crypto projects draw their value from solving problems that can’t be solved through TradFi mechanisms, their tokenomics should reflect that. 

The industry standard, developed by McKinsey & Co. and adapted to token engineering purposes by Outlier Ventures, is structuring the problem through a logic tree, following MECE.
MECE stands for Mutually Exclusive, Collectively Exhaustive. Mutually Exclusive means that problems in the tree should not overlap. Collectively Exhaustive means that the tree should cover all issues.

In practice, the “Problem” should be replaced by a whole problem statement worksheet. The same will hold for some of the boxes.
A commonly used tool for designing these kinds of diagrams is the Miro whiteboard.

Identifying Stakeholders and Value Flows in Token Engineering

This part is about identifying all relevant actors in the ecosystem and how value flows between them. To illustrate what we mean let’s consider an example of NFT marketplace. In its case, relevant actors might be sellers, buyers, NFT creators, and a marketplace owner. Possible value flow when conducting a transaction might be: buyer gets rid of his tokens, seller gets some of them, marketplace owner gets some of them as fees, and NFT creators get some of them as royalties.

Incentive Mechanisms Canvas

The last part of what we consider to be in the Discovery Phase is filling the Incentive Mechanisms Canvas. After successfully identifying value flows in the previous stage, token engineers search for frictions to desired behaviors and point out the undesired behaviors. For example, friction to activity on an NFT marketplace might be respecting royalty fees by marketplace owners since it reduces value flowing to the seller.

source: https://www.canva.com/design/DAFDTNKsIJs/8Ky9EoJJI7p98qKLIu2XNw/view#7

Design Phase of Token Engineering Process

The second stage of the Token Engineering Process is the Design Phase in which you make use of high-level descriptions from the previous step to come up with a specific design of the project. This will include everything that can be usually found in crypto whitepapers (e.g. governance mechanisms, incentive mechanisms, token supply, etc). After finishing the design, token engineers should represent the whole value flow and transactional logic on detailed visual diagrams. These diagrams will be a basis for creating mathematical models in the Deployment Phase. 

Token Engineering Artonomous Design Diagram
Artonomous design diagram, source: Artonomous GitHub

Objective Function

Every crypto project has some objective. The objective can consist of many goals, such as decentralization or token price. The objective function is a mathematical function assigning weights to different factors that influence the main objective in the order of their importance. This function will be a reference for machine learning algorithms in the next steps. They will try to find quantitative parameters (e.g. network fees) that maximize the output of this function.
Modified Metcalfe’s Law can serve as an inspiration during that step. It’s a framework for valuing crypto projects, but we believe that after adjustments it can also be used in this context.

Deployment Phase of Token Engineering Process

The Deployment Phase is final, but also the most demanding step in the process. It involves the implementation of machine learning algorithms that test our assumptions and optimize quantitative parameters. Token Engineering draws from Nassim Taleb’s concept of Antifragility and extensively uses feedback loops to make a system that gains from arising shocks.

Agent-based Modelling 

In agent-based modeling, we describe a set of behaviors and goals displayed by each agent participating in the system (this is why previous steps focused so much on describing stakeholders). Each agent is controlled by an autonomous AI and continuously optimizes his strategy. He learns from his experience and can mimic the behavior of other agents if he finds it effective (Reinforced Learning). This approach allows for mimicking real users, who adapt their strategies with time. An example adaptive agent would be a cryptocurrency trader, who changes his trading strategy in response to experiencing a loss of money.

Monte Carlo Simulations

Token Engineers use the Monte Carlo method to simulate the consequences of various possible interactions while taking into account the probability of their occurrence. By running a large number of simulations it’s possible to stress-test the project in multiple scenarios and identify emergent risks.

Testnet Deployment

If possible, it's highly beneficial for projects to extend the testing phase even further by letting real users use the network. Idea is the same as in agent-based testing - continuous optimization based on provided metrics. Furthermore, in case the project considers airdropping its tokens, giving them to early users is a great strategy. Even though part of the activity will be disingenuine and airdrop-oriented, such strategy still works better than most.

Time Duration

Token engineering process may take from as little as 2 weeks to as much as 5 months. It depends on the project category (Layer 1 protocol will require more time, than a simple DApp), and security requirements. For example, a bank issuing its digital token will have a very low risk tolerance.

Required Skills for Token Engineering

Token engineering is a multidisciplinary field and requires a great amount of specialized knowledge. Key knowledge areas are:

  • Systems Engineering
  • Machine Learning
  • Market Research
  • Capital Markets
  • Current trends in Web3
  • Blockchain Engineering
  • Statistics

Summary

The token engineering process consists of 3 steps: Discovery Phase, Design Phase, and Deployment Phase. It’s utilized mostly by established blockchain projects, and financial institutions like the International Monetary Fund. Even though it’s a very resource-consuming process, we believe it’s worth it. Projects that went through scrupulous design and testing before launch are much more likely to receive VC funding and be in the 10% of crypto projects that survive the bear market. Going through that process also has a symbolic meaning - it shows that the project is long-term oriented.

If you're looking to create a robust tokenomics model and go through institutional-grade testing please reach out to contact@nextrope.com. Our team is ready to help you with the token engineering process and ensure your project’s resilience in the long term.

FAQ

What does token engineering process look like?

  • Token engineering process is conducted in a 3-step methodical fashion. This includes Discovery Phase, Design Phase, and Deployment Phase. Each of these stages should be tailored to the specific needs of a project.

Is token engineering meant only for big projects?

  • We recommend that even small projects go through a simplified design and optimization process. This increases community's trust and makes sure that the tokenomics doesn't have any obvious flaws.

How long does the token engineering process take?

  • It depends on the project and may range from 2 weeks to 5 months.

Tagi