Large Language Model Guided Self-Debugging Code Generation
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.
67a4213c54bfb820ffb26f4a
Muntasir Adnan
claimed_verified
2025-02-06T14:15:07.849Z
65eef9ce7443c09267513796
/avatars/62547f99130557f54093b2ff4d6c9c24.svg
Muntasir Adnan
user
adnaan525
67a4213c54bfb820ffb26f4b
Zhiwei Xu
admin_assigned
2025-02-06T16:13:47.448Z
659fa5d10183046e16b6f993
/avatars/744497ac023d9ebc21e0c297d4f15fca.svg
Zhiwei Xu
user
zhiwei555
67a4213c54bfb820ffb26f4c
Carlos C. N. Kuhn
admin_assigned
2025-02-06T16:13:58.336Z
6510df53469c325dc4dc69a5
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/NBydVZ-3cc8ORQpQeQCf_.png
Carlos Kuhn
user
CarlosKuhn
Large Language Model Guided Self-Debugging Code Generation
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.
PyCapsule framework improves Python code generation efficiency and correctness through a two-agent pipeline and self-debugging modules, demonstrating superior performance on HumanEval, HumanEval-ET, and BigCodeBench benchmarks.
prompt inference iterative error handling case testing self-debugging modules