The growing interest in applying Artificial Intelligence (AI) to software engineering has accelerated since the release of ChatGPT 3.5 in 2022. This paper investigates how Large Language Models (LLMs) support applications’ modular development and testing. We introduce AIPyCraft, a novel AI-assisted framework that facilitates the end-to-end lifecycle of software projects. Our approach leverages Google Gemini 2.5 Pro model to generate, correct, and manage software components within a semi-automated and incremental workflow. AIPyCraft enables project creation, environment setup, error correction, and feature evolution in an integrated manner. We develop and test a blockchain-based Oracle component designed for 6G wireless network environments, i.e., a complex, real-world scenario that demands secure data integration and modular extensibility. Preliminary experiments demonstrate AIPyCraft’s potential to accelerate small-scale software project development through an “understand-by-building” methodology. Our findings show that using an LLM to generate effective TOML jobs for Off-chain 6G functions is feasible, with an average of 1.05 iterations to correct the TOML code and mean experiment time of 27.8 seconds.

1. AIPyCraft
AI-Assisted Software Development Lifecycle for 6G Blockchain Oracle
Validation
Antonio M. Alberti - School of Computer Science, Univ. of Leeds, UK
Alexis Leal, Ariel Dalla Costa, Cristiano Bonato Both - Unisinos, Brazil

2. Outline
• Motivations
• Contextualization
• Contributions
• Architecture and Workflow
• Evaluation
• Lessons Learned
• Conclusion and Future Work
Antonio Alberti 2025 AIPyCraft 2 / 16

3. Motivations
• ChatGPT 3.5 sparked public interest in LLMs for code generation.
• However, manual prompting, copying, and testing is tedious and unscalable.
• Need for autonomous agents to generate, test, and refine code incrementally.
• AIPyCraft proposes an LLM-powered tool for modular, incremental development.
• Research question:
◦ Can a tool like this actually help in the development of code components for 6G?
Antonio Alberti 2025 AIPyCraft 3 / 16

4. Disrutive 6G (D6G) Architecture to Integrate On-chain and Off-chain 6G
Services
Operator 1
Node
Operator N
Node
Operator 3
Node
Operator 2
Node
DLT (On-Chain)
Off-Chain
Oracle
Jobs
External
Data
External
Data
External
Data
External
Data
External
Data
External
Data
Figure: On the left, public DLT nodes register smart contracts that run code in an immutable way
when monetized. On the center, an off-chain oracle receives data requests (“jobs”) from the DLT
ledger and dispatches them to various external off-chain data sources in the right — including
network slicing as a service. The oracle then returns verified responses back to the DLT.
Antonio Alberti 2025 AIPyCraft 4 / 16

5. Contributions
• Open-source development automation: uses LLM APIs to generate code, manage
virtual environments, iteratively correct errors, and integrate components.
• Innovative application on 6G component design: explores the role of LLMs on the
development of next generation virtual network functions.
• New evaluation tools and methodology: assesses whether LLMs can indeed help on
6G software components development.
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6. AIPyCraft Architecture
AIConnector
GPT-o3-
mini
Gemini
2.5
LLM APIs
CLI
Queries and
responses
from LLM API
AICodeParser
InstallationScriptGenerator
SolutionCreator SolutionLoader
SolutionRunner
SolutionDisplay
SolutionCorrecting,
ComponentCorrector
SolutionImporter
SolutionUpdater
SolutionFeatureAdding
Dispatcher
Operating system
call for main.py
and feedback
Second method
of correcting a solution.
Install Python
virtual
environment in a
solution.
Get user prompts
and show results.
User asks for a new
feature in a solution.
First method of
correcting a solution
or component.
Show the solution code
in the screen.
Creates a new solution
and its components.
...
Solutions
Folder
AIPyCraft
Load a solution from
file.
Import an existing
folder as a solution
Component
Solution
Component
Component
...
Component
Solution
Component
Component
...
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7. AIPyCraft Workflow
Start
SolutionCreator
No Approve?
Create components
Determine required virtual
environment
InstallationScriptGenerator
SolutionRunner
No
Yes, user explains
the problem
Yes
Problem?
SolutionCorrecting,
SolutionUpdate or
ComponentCorrector
Solution
Feature
Adding
End
No
Yes
New
feature?
• User inputs solution/feature
description.
• LLM generates components
incrementally.
• Virtual environments created per
solution.
• Code run and errors corrected
iteratively.
• Human-in-the-loop for validation and
feature additions.
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8. Proof-of-Concept: TOML Script Correction
• Tested with Google Gemini 2.5 Pro on Chainlink off-chain oracle jobs.
• Automated multi-trial testing with syntax and semantic scenarios.
• Two TOML Script Tester were developed to test:
◦ Scenario 1: TOML syntax evaluation using Tomli Python package.
◦ Scenario 2: TOML semantic evaluation using a real Chainlink node.
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9. Proof-of-Concept Evaluation
ComponentCorrector
CLI
run_tester_multiple
Run
experiment
tester.py
20 trials
AIConnector
SolutionLoader
SolutionRunner
SolutionUpdater
AIPyCraft
Solution
config.toml
main.py
main.py
tomli
1
2
Scenario 1
Scenario 2
TOML Script Tester
1
2
Job
Chainlink
Node
LLM APIs
Up to 20 correction
tentatives
Figure: Automated testing setup with AIPyCraft, Gemini 2.5 Pro, and Chainlink node.
Antonio Alberti 2025 AIPyCraft 9 / 16

10. Proof-of-Concept Results: Scenario 1 - Syntax Correction
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1.0
Iterations
to
Success
Failed Trials: 0
Mean Iterations (95% CI)
Figure: Number of interactions required to correct TOML job syntax using tomli.
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11. Proof-of-Concept Results: Scenario 1 - Time required to correct
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Total
Test
Duration
(s)
Failed Trials: 0
Mean Duration (95% CI)
Figure: Time required to correct TOML job syntax using tomli.
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12. Proof-of-Concept Results: Scenario 2 - Semantic Correction
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to
Success
Failed Trials: 0
Mean Iterations (95% CI)
Figure: Number of interactions required to correct TOML job semantics on Chainlink node.
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13. Proof-of-Concept Results: Scenario 2 - Time required to correct
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Total
Test
Duration
(s)
Failed Trials: 0
Mean Duration (95% CI)
Figure: Time required to correct TOML job semantics on Chainlink node.
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14. Lessons Learned
• Prompting LLMs to create modular solutions is challenging.
• Hallucinations and redundant code require iterative/interative corrections.
• To employ modular design with LLMs enforces reusability and debugging.
• Human validation remains critical in AI-assisted coding. At least for now... Who
knows?
• Virtual environments separation prevents dependency conflicts, which is difficult to be
solve by LLMs.
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15. Conclusion
• AIPyCraft successfully integrated LLMs for modular 6G component development.
• Simple TOML jobs generated and corrected with acceptable performance.
• LLMs supports human-machine collaboration and incremental development.
• Future work: compare LLMs, more complex Jobs, other D6G components, security,
other languages.
• Emergence of an ’understand-by-building’ software engineering, in which strategy,
design, coding, debugging, and optimization are rapidly co-created with machines.
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16. Thank you!
Thank you!
Antonio Alberti
A.M.Alberti@leeds.ac.uk
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