Execution-Guided Code Generation

Overview

Execution-guided code generation uses evidence from running programs—such as test results, compiler errors, runtime traces, and rendered outputs—to help language models produce and repair code. Common approaches iteratively revise a candidate, generate or refine tests, select among candidates by comparing behavior, or train models with execution-based rewards and step-level feedback. These methods are evaluated on settings ranging from programming benchmarks such as HumanEval, MBPP, and LiveCodeBench to repository tasks, database queries, and generated interfaces. A key tension is that execution feedback can catch syntax and runtime failures but may be noisy, costly, or misleading when tests are incomplete or the model and verifier share a mistaken assumption.

Latest papers

CardsList

67 papers8 papers in the last four weeks, up 100% on the four weeks before. 0.0% of all new papers.

  1. Self-Execution Simulation Improves Coding Models

    Mar 11, 2026Gallil Maimon, Ori Yoran, Felix Kreuk +4Execution-Guided Code GenerationLLM Self-Correction

  2. SPARC: Scenario Planning and Reasoning for Automated C Unit Test Generation

    Feb 18, 2026Jaid Monwar Chowdhury, Chi-An Fu, Reyhaneh JabbarvandAutomated Test GenerationExecution-Guided Code Generation

  3. Re4: Scientific Computing Agent with Rewriting, Resolution, Review and Revision

    Aug 28, 2025Ao Cheng, Lei Zhang, Guowei HeScientific Code GenerationExecution-Guided Code Generation

  4. Toward Secure Code Generation: Bridging Correctness and Security via Task-Adaptive Vulnerability Modeling and Execution-Based Benchmarking

    Jul 2, 2024Jiexin Wang, Liuwen Cao, Xitong Luo +6Execution-Guided Code GenerationAgentic Code Generation