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DepShim: Automated Compatibility Layer Injection Service

A managed service that analyzes a legacy Python codebase, identifies incompatible dependencies, and deploys a thin compatibility shim layer that rewrites function signatures, module paths, and deprecated APIs at runtime to make the code run on newer Python versions without touching the original source. Teams submit their repo URL or tarball; DepShim returns a containerized environment with the shim pre-installed and a compatibility report showing what was patched and what needs manual review.

SERVICE

31 weeks • 70% confidence

Value Proposition

Eliminates the false choice between staying vulnerable or rewriting code. Buys 12-24 months of runway to upgrade incrementally without forking or maintaining two versions. Faster and cheaper than hiring contractors to refactor; less risky than hoping upstream maintainers update.

Target Audience

Mid-market tech companies and open-source maintainers stuck on Python 2.7, 3.5, or 3.6 with critical codebases they can't rewrite; teams with 50K-500K lines of legacy code

Key Features

  • Automated AST-based dependency graph analysis to identify breaking changes
  • Pre-built shim library for 200+ common packages (Django, SQLAlchemy, Numpy, Requests, etc.) with version-specific patches
  • Runtime monkey-patching that intercepts deprecated calls and translates them to modern equivalents
  • And more, with full implementation detail...

Tech Stack

Python AST library (ast module) Dependency parsing (pip-audit, pipdeptree) Docker & Docker Compose Git (for repo cloning and analysis)
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Original Problem

Legacy Python codebases blocked from upgrading to newer Python versions due to incompatible dependencies

Developers maintaining projects with outdated dependencies (like Caffe-TensorFlow bridges) cannot upgrade to Python 3.5+ because critical libraries lack compatibility. This creates security vulnerabilities, prevents access to performance improvements, and blocks team members from using modern Python tooling. Current solutions require either forking unmaintained projects or rewriting entire components.

Score: 17.5%