Developer and User Experience

i) Enabling Non-Technical Users to Create Smart Contracts with NLP on aelf

The complexity of creating smart contracts in aelf, presents significant challenges for non-technical users or even technical users who may not be familiar with the Web3 environment. Users often struggle with understanding programming languages, navigating development environments, and ensuring the security and functionality of their contracts. By leveraging Natural Language Processing (NLP), aelf can simplify this process, allowing users to create end to end smart contracts through intuitive natural language inputs.

Simplifying Smart Contract Creation

NLP can transform the way non-technical users interact with blockchain technology by interpreting natural language descriptions and converting them into executable smart contract code. Users can describe their contractual needs in plain language, such as "Create an escrow contract where Alice deposits 10 ELF, and Bob receives it upon satisfying X condition" The NLP model can then interpret this input, extract relevant entities and conditions, and generate the corresponding smart contract code. This process leverages the capabilities of advanced NLP models like GPT-4 and OpenAI's Codex, which are trained to understand and generate code based on natural language descriptions. These models can map user inputs to predefined templates or dynamically generate code structures that match the user's requirements. This allows non-technical users to create complex smart contracts without needing to write a single line of code.

Enhancing Accessibility and Usability

By integrating NLP into its platform, aelf intends to significantly enhance the accessibility and usability of blockchain technology. This democratisation of technology enables a broader audience to participate in the blockchain ecosystem, driving adoption and innovation. For example, consider a user who wants to set up a simple DeFi contract for staking tokens. Instead of hiring a developer, the owner can use an NLP-driven interface to describe the terms of the contract. The system interprets these terms and generates a smart contract that handles the various staking functionalities, ensuring that the DeFi protocol works as intended and most importantly, secure.

Real-Time Feedback and Validation

Another key benefit of using NLP for smart contract creation on aelf is the provision of real-time feedback and validation. As users input their requirements, the NLP system can highlight potential issues, suggest optimisations, and explain complex logic in simple terms. This interactive process ensures that users understand the generated code and can make informed decisions about their contracts. For instance, if a user inputs a contract description that could lead to a security vulnerability, the NLP system can flag this and provide suggestions for improvement. This not only enhances the security of the contracts but also educates users about best practices in smart contract development.

Broadening the User Base

Implementing NLP for smart contract creation allows aelf to broaden its user base by catering to individuals and businesses without technical backgrounds. This inclusivity is crucial for the widespread adoption of blockchain technology. By lowering the barriers to entry and simplifying the process, aelf intends to attract a diverse range of users who can benefit from the automation and security of smart contracts without needing to become blockchain experts.

ii) AI based Smart Contract Audit for Developers on aelf

AI auditing of smart contracts leverages large-scale pre-trained models, such as GPT-4 and BERT, to automate the audit process. Unlike traditional audits, which rely on manual code reviews and static analysis tools, AI auditing utilises natural language processing (NLP) and deep learning to deliver more comprehensive and accurate audit reports.

In AI auditing, smart contract code is input into a pre-trained model, which analyses the code's syntax and logical structure to identify security vulnerabilities and logical errors. Users interact with the model using prompts to guide the analysis, such as "Check for reentrancy attack vulnerabilities in this smart contract." The model then performs a detailed analysis and generates an audit report.

The audit report details potential vulnerabilities and provides corresponding remediation suggestions.

Key Aspects of AI based Smart Contract Audits

The primary goal is to review and analyse the code before deployment to identify vulnerabilities, inefficiencies, or potential leaks. This process is critical, as defects can lead to significant financial losses or security breaches. For instance, audits of DeFi application contracts rigorously test for exploits to protect user funds.

Vulnerability Assessment:

Identifying potential security issues in the code. For example, an audit of a token issuance contract uncovered a vulnerability that could allow fund theft, enabling developers to resolve the issue pre-launch.

Code Optimisation:

Improving efficiency and reducing gas fees. Auditors recommended optimisations for a game DApp's contract, significantly lowering transaction fees and enhancing accessibility and affordability for players.

Compliance Check:

Ensuring adherence to legal and regulatory standards. Audits verify that smart contracts meet new regulatory requirements, ensuring DeFi platforms remain compliant.

Technical Implementation on aelf

This process typically involves two approaches: prompt design and fine-tuning.

1. Prompt Design Approach

The prompt design approach involves constructing specific prompts to guide the model to perform optimally in certain tasks without requiring retraining. In the context of smart contract auditing, the steps are as follows:

1. Define the Target Task: Clearly outline the specific issues to be audited and establish an audit rule library. For example:

   • Reentrancy Attack Vulnerability: Determine if the contract allows external contracts to repeatedly call, leading to multiple fund transfers.

2. Design Prompts: Create precise prompts to guide the model's analysis. For example:

   • "Check if this smart contract has reentrancy attack vulnerabilities."

3. Input Prompts and Contract Code: Enter the designed prompts and smart contract code into a large model, such as GPT-4. The steps include:

   • Select a model that supports code analysis.
   • Input the prompts specifying the exact issues to be checked.

4. Generate Audit Report: The model analyses the input prompts and contract code, generating an audit report that typically includes:

   • Vulnerability Description: Detailed description of discovered vulnerabilities, including the exact location and trigger conditions.
   • Risk Assessment: Evaluation of the severity and potential impact of the vulnerability.
   • Remediation Suggestions: Specific recommendations for fixing the vulnerability, such as modifying code logic, adding access controls, or using secure libraries.

This method's advantage is that it does not require extensive data and computational resources, allowing for quick deployment and application on aelf.

2. Automated Fine-Tuning Approach

The fine-tuning approach involves retraining the large model to better adapt it to specific tasks. This requires a significant amount of historical audit data and remediation cases to improve the model's auditing capabilities. The steps include:

1. Data Collection: Gather a large dataset of smart contract codes, audit reports, and remediation cases.
2. Data Preprocessing: Clean and format the data to ensure effective learning.
3. Model Fine-Tuning: Train the large model on the preprocessed dataset to enhance its understanding and auditing capabilities.
4. Validation and Optimisation: Evaluate the model's performance using a validation set and make necessary optimisations.
5. Model Deployment: Deploy the fine-tuned model in the actual audit environment.

This method can significantly enhance the model's performance in specific tasks but requires substantial computational resources and time. The fine-tuned model can more accurately identify complex vulnerabilities in smart contracts and provide targeted remediation suggestions.

Ultimately, this model will be integrated into a smart contract auditing tool, enabling both aelf and dApp developers to utilise it for thorough audits prior to deploying their smart contracts on the aelf chain.

iii) Enhancing User and Developer Experience with AI-Powered Chatbots on aelf

Integrating AI-powered chatbots into the aelf blockchain ecosystem can significantly enhance both user and developer experiences by providing real-time support, automating routine tasks, and facilitating access to information.

User Experience Improvement

AI-powered chatbots can dramatically improve user experience by offering instant support and personalised assistance. For instance, users new to the aelf ecosystem often have questions about setting up wallets, conducting transactions, or understanding blockchain features. aelf has partnered with an industry leading strategic partner, Chaingpt, to develop an AI chatbot that can provide 24/7 support, answering frequently asked questions, guiding users through processes step-by-step, and troubleshooting issues in real-time. This immediate assistance reduces frustration and enhances user satisfaction. This is commonplace in traditional businesses such as major banks like Bank of America that have implemented AI chatbots like Erica to assist customers with everyday banking needs, providing transaction histories, sending alerts, and offering financial advice. Similarly, an AI chatbot on aelf can help users navigate the platform more efficiently, providing information about token balances, transaction statuses, and smart contract functionalities.

AI chatbots can also enhance personalised user engagement on the aelf platform. By analysing user behaviour and preferences, chatbots can offer tailored recommendations, such as suggesting new dApps or features that might interest the user. This personalised interaction can increase user engagement and retention, making the platform more attractive to both new and existing users. This is commonly done in e-commerce platforms like Amazon that use AI chatbots to recommend products based on user browsing history and past purchases. Applying this to aelf, the chatbot could recommend specific dApps or new blockchain features based on the user’s activity, thus enhancing the user’s interaction with the platform

Developer Experience Enhancement

For developers, AI-powered chatbots can streamline the development process by automating repetitive tasks and offering code assistance. aelf is developing integrated chatbots that provide real-time coding help, debug suggestions, and documentation access. For instance, when a developer encounters an error while writing smart contracts in C#, the chatbot can instantly suggest solutions, recommend best practices, and provide snippets from relevant documentation. GitHub Copilot, an AI-powered code completion tool developed by OpenAI and GitHub, serves as an excellent example. It assists developers by suggesting code lines and completing functions based on the context. By integrating similar AI capabilities, aelf can make the development process more efficient, helping developers quickly resolve issues and focus on creating innovative applications. We want our developers to spend less time on coding, and more time on creating value.

Efficient Onboarding and Education

AI chatbots can facilitate efficient onboarding and continuous education for both users and developers. New users can be guided through the initial setup, learning about key features and functionalities through interactive conversations with the chatbot. For developers, the chatbot can provide tutorials, best practices, and updates on new development tools and protocols within the aelf ecosystem. Educational institutions and online learning platforms like Coursera use AI chatbots to guide students through course selections, answer questions, and provide feedback. Similarly, aelf’s chatbot can act as a mentor, ensuring users and developers have a smooth and informative onboarding experience, reducing the learning curve associated with blockchain technology.