Concrete Type Inference Data for Code Optimization with ML and SMT

Dataset and model files for the paper "Concrete Type Inference for Code Optimization using Machine Learning with SMT Solving". The data was created by Fangke Ye at the Georgia Institute of Technology. The specific size, row count, and file formats are not detailed in the available metadata.

Use Cases

• Training ML models for type inference based on code features mentioned in the description
• Benchmarking SMT solvers in code optimization tasks as suggested by the paper's focus
• Analyzing the relationship between concrete types and optimization opportunities in programs

Strengths

• Associated with a specific, peer-reviewed academic paper from Georgia Institute of Technology
• Explicitly linked to the research domains of machine learning and SMT solving for code optimization

Limitations

• Description metadata is limited; actual data quality requires manual inspection after download
• Row count is unknown, which may limit suitability assessment
• Column-level documentation is absent; field semantics must be inferred after download

Provenance

Source

Fangke Ye, Georgia Institute of Technology

Collection Method

Likely generated for the associated research paper on concrete type inference.