Test-Driven Generation of CSPs Using LLMs
PaperFirst seen 7/7/2026
Last seen 7/7/2026
Evidence 12 chunks
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28 connectionsThe paper evaluates its approach by generating and verifying CSP models for the TSP.
The paper mentions MCP-Solver as a related framework based on the Model Context Protocol.
The paper evaluates its approach by generating and verifying CSP models for the N-Queens problem.
The paper applies test-driven development principles to automate CSP knowledge base generation.
The paper uses PyChoco as the Python API for constraint solving.
The paper introduces an automated approach to knowledge base generation for CSPs.
The paper uses semantic validation via test cases to verify the correctness of generated CSPs.
The paper uses one-shot learning by providing one code example in the prompt.
The paper introduces an iterative architecture where LLM-generated code is executed and errors fed back in a loop.
The paper mentions LangChain as a similar reasoning framework but chooses to implement its own loop.
The paper mentions Chain of Thought as a related reasoning framework.
The paper mentions ReAct as a related reasoning framework.
The paper mentions the Model Context Protocol as basis for related frameworks.
The paper mentions SATzilla as a portfolio-based solver framework in the related work section.
The paper mentions Protégé as an example of graphical ontology representation in related work.
The paper mentions ChatGPT as an example LLM used in software development.
The paper mentions GitHub Copilot as an example LLM tool for software development.
The paper references GPT-Engineer as an example of LLM-based code generation.
Christian Bähnisch is listed as a co-author of the paper.
The paper mentions the Program of Thoughts approach as related work using LLM and Python interpreter.
Lothar Hotz is listed as a co-author of the paper.
Alexander Felfernig is listed as a co-author of the paper.
Sebastian Lubos is listed as a co-author of the paper.
The paper leverages LLMs to generate CSP knowledge bases.
The implementation uses ChatGPT-4.1 as the LLM for generating constraint models.
The paper uses syntactic validation by checking for compile errors/exceptions in generated code.
The paper evaluates its approach by generating and verifying CSP models for the Magic Square problem.
The paper evaluates its approach by generating and verifying CSP models for the Map Coloring problem.