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Found 5 papers, 2 papers with code
Ranking LLM-Generated Loop Invariants for Program Verification
In this work, we observe that Large Language Models (such as gpt-3. 5 or gpt-4) are capable of synthesizing loop invariants for a class of programs in a 0-shot setting, yet require several samples to generate the correct invariants.
Can Large Language Models Transform Natural Language Intent into Formal Method Postconditions?
The emergent abilities of Large Language Models (LLMs) have the potential to facilitate the translation of natural language intent to programmatically checkable assertions.
Towards Generating Functionally Correct Code Edits from Natural Language Issue Descriptions
Several benchmarks have recently emerged to evaluate the ability of LLMs to generate functionally correct code from natural language intent with respect to a set of hidden test cases.
Interactive Code Generation via Test-Driven User-Intent Formalization
Large language models (LLMs) have shown great potential in automating significant aspects of coding by producing natural code from informal natural language (NL) intent.
Program Merge Conflict Resolution via Neural Transformers
Our model achieves 63-68% accuracy for merge resolution synthesis, yielding nearly a 3x performance improvement over existing semi-structured, and 2x improvement over neural program merge tools.
