https://github.com/saujasv/generating-pragmatic-examples
공식 코드Generating Pragmatic Examples to Train Neural Program Synthesizers
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
Program synthesis is the process of automatically generating a program or code snippet that satisfies a given specification or set of requirements. This can include generating code from a formal specification, a natural language description, or example inputs and outputs. The primary goal of program synthesis is to minimize human intervention in the coding process, reduce errors, and improve productivity.
Program synthesis often involves the use of advanced algorithms, artificial intelligence, and machine learning techniques to search the space of possible programs that meet the given constraints. This process can be guided by a variety of techniques, such as constraint solving, symbolic execution, and genetic algorithms.
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Generating Pragmatic Examples to Train Neural Program Synthesizers
TraceFL: Interpretability-Driven Debugging in Federated Learning via Neuron Provenance
An Influence-based Approach for Root Cause Alarm Discovery in Telecom Networks
Learning to Combine Per-Example Solutions for Neural Program Synthesis
SeMantic AnsweR Type prediction task (SMART) at ISWC 2020 Semantic Web Challenge
RepairAgent: An Autonomous, LLM-Based Agent for Program Repair
Path Analysis for Effective Fault Localization in Deep Neural Networks
AequeVox: Automated Fairness Testing of Speech Recognition Systems
Uncertainty-Aware Probabilistic Graph Neural Networks for Road-Level Traffic Accident Prediction
A Multi-Granularity-Aware Aspect Learning Model for Multi-Aspect Dense Retrieval
Learning, Fast and Slow: A Goal-Directed Memory-Based Approach for Dynamic Environments
Invalidator: Automated Patch Correctness Assessment via Semantic and Syntactic Reasoning
Towards Reliable Evaluation of Neural Program Repair with Natural Robustness Testing
Explaining Image Classifiers using Statistical Fault Localization
CompilerDream: Learning a Compiler World Model for General Code Optimization
Comparison of Syntactic and Semantic Representations of Programs in Neural Embeddings
RePair: Automated Program Repair with Process-based Feedback
Can GPT-O1 Kill All Bugs? An Evaluation of GPT-Family LLMs on QuixBugs
PoE-World: Compositional World Modeling with Products of Programmatic Experts
CLEVER: A Curated Benchmark for Formally Verified Code Generation
CLEVER: A Curated Benchmark for Formally Verified Code Generation
Memorization and Generalization in Neural Code Intelligence Models
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.