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Notes on code generation
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TDD-Agent: Test-Driven Reasoning for Code Generation
Researchers introduce TDD-Agent, a system that uses the test-driven development (TDD) paradigm for code generation. The system first generates tests to clarify expected behaviors before implementation and then refines…
Generative Compilation: On-the-Fly Compiler Feedback as AI Generates Code
Researchers introduce 'generative compilation', an approach to get feedback from compilers on partially generated code during the code generation process. The method aims to reduce errors and improve the functionality…
ProjAgent: Procedural Similarity Retrieval for Repository-Level Code Generation
ProjAgent is a new system for generating code at the repository level. It introduces a method called 'procedural similarity' to retrieve relevant functions from the repository, even if those functions have different…
ProjAgent: Procedural Similarity Retrieval for Repository-Level Code Generation
The paper proposes ProjAgent, a new system for generating code at the repository level by introducing 'procedural similarity' as a retrieval signal. This method decomposes the target function into steps and retrieves…
ProjAgent: Procedural Similarity Retrieval for Repository-Level Code Generation
The paper introduces ProjAgent, a system for generating code at the repository level that considers procedural similarity as a new method for retrieving appropriate functions. ProjAgent deconstructs target functions…
ProjAgent: Procedural Similarity Retrieval for Repository-Level Code Generation
The paper proposes ProjAgent, a system for generating code at the repository level. It introduces 'procedural similarity' as a new way to retrieve relevant functions, addressing a limitation of existing methods that…
Reasoning effort, not tool access, buys first-try reliability in agentic code generation: an observational study
A study examined the impact of additional capabilities on agentic coding assistants in terms of software quality and reliability. The results showed that while high-tier models performed well, a low-cost local model…
Reasoning effort, not tool access, buys first-try reliability in agentic code generation: an observational study
A study found that the level of reasoning effort in agentic code generation, rather than the availability of additional capabilities like testing tools and design prompts, significantly affects the reliability of…
Reasoning effort, not tool access, buys first-try reliability in agentic code generation: an observational study
A study compared different agentic coding assistants and their abilities in building a real-time retrospective board. The results showed that the level of reasoning effort, rather than access to additional tools or…
Reasoning effort, not tool access, buys first-try reliability in agentic code generation: an observational study
A study found that more reasoning effort in agentic code generation (AI programs assisting human coders) leads to higher reliability and fewer errors, rather than the use of additional tools or capabilities.
AxDafny: Agentic Verified Code Generation in Dafny
Researchers present AxDafny, a framework for generating executable code and proof artifacts in Dafny using a verifier-guided repair method. The study uses a benchmark called LiveCodeBench-Pro-Dafny with 250 programming…
ManimAgent: Self-Evolving Multimodal Agents for Visual Education
Researchers present ManimAgent, a self-evolving multimodal agent that uses a dual-channel Episodic Memory Bank to carry reflection experience across tasks in generating Python code using the Manim library for…
ManimAgent: Self-Evolving Multimodal Agents for Visual Education
The research presents ManimAgent, a self-evolving multimodal agent that learns from its failures and successes in generating Python code using the Manim library to render mathematical animations. The agent stores these…
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