Temporal Reasoning for API Deprecation and Migration
API deprecation is not a single event but an ongoing, contextual process that requires long-term adaptation and understanding of migration patterns over time.
Libshift models API migration as a temporal knowledge graph, capturing relationships between deprecated methods and their replacements across different versions.
Supports complex migration patterns through multi-hop mappings, version-aware reasoning, and evidence tracing from multiple sources including docstrings and code.
Pattern: append → concat
# Deprecated approach
df1.append(df2)
# Recommended replacement
pd.concat([df1, df2])Simple direct replacement where one deprecated method maps to a single recommended alternative.
Pattern: lookup → melt, factorize
# Deprecated approach
df.lookup(row_indexer, col_indexer)
# Multiple replacement options
df.melt() # For reshaping data
pd.factorize(df[col]) # For categorical encodingComplex migration where one deprecated method has multiple context-dependent replacements.
Pattern: where → applymap → map
# Original deprecated method
df.where(condition)
# Intermediate replacement (also deprecated)
df.applymap(func)
# Current recommended approach
df.map(func)Multi-step migration path where replacements themselves become deprecated over time.
Version-aware edges that capture the evolution of API recommendations across different library versions and time periods.
Advanced mapping extraction using Perplexity Sonar and Qwen models to identify deprecation patterns from documentation.
Comprehensive analysis of docstrings, comments, and code to build robust deprecation mappings with high confidence.
Sophisticated confidence and decay scoring system for migration edges to prioritize the most reliable recommendations.
393 methods and 214 validated migration edges covering pandas and pydantic libraries with rich temporal metadata.
Full traceability from source documentation to final recommendations, enabling verification and quality assessment.