Papers with Code 아카이브
Program Repair
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 6
- 평가표
- 4
- 보존된 평가 기록
- 8
Task of teaching ML models to modify an existing program to fix a bug in a given code.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/amazon-science/self_debug
https://github.com/aorwall/swe-bench-docker
https://github.com/autocoderoversg/auto-code-rover
https://github.com/chengjunyan1/gn-transformer-ast
Graph Conditioned Sparse-Attention for Improved Source Code Understanding
https://github.com/coinse/arachne
https://github.com/deepsoftwareanalytics/omnigirl
https://github.com/eth-sri/TFix
https://github.com/experepair/experepair
Agentless: Demystifying LLM-based Software Engineering Agents
https://github.com/gonglinyuan/safim
https://github.com/google-research-datasets/eth_py150_open
https://github.com/kagnlp/CodeGenerator
https://github.com/kth/chai
SequenceR: Sequence-to-Sequence Learning for End-to-End Program Repair
https://github.com/liqs-v2/ctim-rover
https://github.com/mdrafiqulrabin/ci-dd-perses
Understanding Neural Code Intelligence Through Program Simplification
https://github.com/ntunlp/execeval
https://github.com/sorendunn/agentless-lite
Agentless: Demystifying LLM-based Software Engineering Agents
https://github.com/terne/dtuproject
Deep Reinforcement Learning for Programming Language Correction
https://github.com/ut-amrl/pips
Robot Action Selection Learning via Layered Dimension Informed Program Synthesis
https://github.com/yangyucheng000/University/tree/main/model-1/plbart
Unified Pre-training for Program Understanding and Generation
https://github.com/zysszy/TreeGen
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.