ShadowBumpAI
Analyze an Upgrade

Methodology & Soundness

How ShadowBump AI reliably detects dependency behavioral regressions and leverages NVIDIA open-source models via Nebius.

1. The Silent Regression Problem

Standard test suites only assert anticipated code paths. When upgrading dependencies (such as migrating from Zod v3 to Zod v4), breaking changes often pass build steps while silently failing in production:

Option Deprecation

required_error is discarded in v4 without throwing compile errors, falling back to generic phrasing.

Property Removal

error.errors is replaced by .issues, causing downstream runtime TypeErrors.

Coercion Semantics

Date and NaN parsing format issue strings differently ("nan" vs "NaN").

2. Difference vs Defect (The Oracle Problem)

A behavioral difference is not automatically a bug. Libraries intentionally modernize APIs and error phrasing. ShadowBump AI categorizes outcomes into four explicit tiers:

● MATCH

Identical output values, validation status, and issue codes across both versions.

● BEHAVIORAL DIFFERENCE

Observable changes in error phrasing or output structure that do not violate an explicit contract.

● POTENTIAL REGRESSION

Validation status flipped, missing properties (error.errors), or dropped custom messages.

● EXECUTION ERROR / INCONCLUSIVE

Subprocess crashed, timed out (5,000ms limit), or threw an unhandled internal exception.

3. Twin Sandbox Isolation Boundary

•Separate Directories: Baseline (zod@3.23.8) and candidate (zod@4.6.7) are installed in independent folders with isolated node_modules.
•Sanitized Environment: Subprocesses are spawned without host credentials or forwarded API keys (NODE_ENV: 'test').
•Execution Timeouts: Subprocesses are forcefully terminated after 5,000ms to eliminate infinite loop risks.

4. NVIDIA Nemotron & Nebius Token Factory

ShadowBump AI connects to Nebius Token Factory querying nvidia/Llama-3.1-Nemotron-70B-Instruct-HF.

The model receives only the minimal empirical evidence package (schema snippet, failing test vector, v3 vs v4 AST output). It does not replace the deterministic diff engine; rather, it synthesizes the root cause and provides a concrete migration code fix.

5. Soundness & Scientific Limitations

• No Difference Observed ≠ Proof of Full Correctness: If a test corpus produces zero differences, it establishes only that the tested inputs behaved identically under the tested runtime. Untested edge cases may still diverge.

• Oracle Dependency: Distinguishing intentional changes from true defects requires an explicit expectation or oracle specification.