arXiv:1701.08466
공식 코드 · Papers with Code
Predicting SMT Solver Performance for Software Verification
원문 초록
The Why3 IDE and verification system facilitates the use of a wide range of Satisfiability Modulo Theories (SMT) solvers through a driver-based architecture. We present Where4: a portfolio-based approach to discharge Why3 proof obligations. We use data analysis and machine learning techniques on static metrics derived from program source code. Our approach benefits software engineers by providing a single utility to delegate proof obligations to the solvers most likely to return a useful result. It does this in a time-efficient way using existing Why3 and solver installations - without requiring low-level knowledge about SMT solver operation from the user.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
AnalogVNN: A fully modular framework for modeling and optimizing photonic neural networks
Evolutionary bagging for ensemble learning
Neural network accelerator for quantum control
Deep Reinforcement Learning for Multi-Agent Interaction
Disparate Censorship & Undertesting: A Source of Label Bias in Clinical Machine Learning
Unsupervised machine learning framework for discriminating major variants of concern during COVID-19
토론
이 논문과 연결된 포럼 글이 아직 없습니다.