Summary of Llm As a Code Generator in Agile Model Driven Development, by Ahmed R. Sadik et al.
LLM as a code generator in Agile Model Driven Development
by Ahmed R. Sadik, Sebastian Brulin, Markus Olhofer, Antonello Ceravola, Frank Joublin
First submitted to arxiv on: 24 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Emerging Technologies (cs.ET); Robotics (cs.RO); Software Engineering (cs.SE)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes an Agile Model Driven Development (AMDD) approach using Large Language Models like GPT4 to auto-generate deployable code. It leverages Model Driven Development (MDD) as a strategy to overcome challenges in natural language descriptions of software, enhancing the flexibility and scalability of the code generation process. The approach is illustrated by modeling a multi-agent Unmanned Vehicle Fleet (UVF) system using Unified Modeling Language (UML), with Object Constraint Language (OCL) for code structure meta-modeling and FIPA ontology language for communication semantics meta-modeling. GPT4 auto-generation capabilities yield Java and Python code compatible with JADE and PADE frameworks, respectively. Evaluation verifies the generated code’s alignment with expected behaviors, identifying enhancements in agent interactions. The study also assesses the complexity of code derived from OCL-only models versus those influenced by both OCL and FIPA ontology meta-models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper uses big language models to help create computer code automatically. This is a big deal because it makes it easier for people to make software without having to write all the code themselves. The problem is that natural language descriptions of software can be unclear, making it hard to generate good code. To solve this, the authors suggest using Model Driven Development (MDD) and Agile MDD (AMDD) approaches. They show how this works by creating a model for a system with multiple vehicles working together, using languages like UML, OCL, and FIPA ontology language. The result is code that can be used in different programming languages like Java and Python. The authors tested the generated code and found it works well. |
Keywords
» Artificial intelligence » Alignment » Semantics
