Large Language Model Guided Self-Debugging Code Generation
Authors: name: Muntasir Adnan, name: Zhiwei Xu, name: Carlos C. N. Kuhn

Automated code generation is gaining significant importance in intelligent
computer programming and system deployment. However, current approaches often
face challenges in computational efficiency and lack robust mechanisms for code
parsing and error correction. In this work, we propose a novel framework,
PyCapsule, with a simple yet effective two-agent pipeline and efficient
self-debugging modules for Python code generation. PyCapsule features
sophisticated prompt inference, iterative error handling, and case testing,
ensuring high generation stability, safety, and correctness. Empirically,
PyCapsule achieves up to 5.7% improvement of success rate on HumanEval, 10.3%
on HumanEval-ET, and 24.4% on BigCodeBench compared to the state-of-art
methods. We also observe a decrease in normalized success rate given more
self-debugging attempts, potentially affected by limited and noisy error
feedback in retention. PyCapsule demonstrates broader impacts on advancing
lightweight and efficient code generation for artificial intelligence systems.