Does a Self-Learning Migration Agent Learn? An Audited Evaluation of Evidence-Graded Experience Memory for Oracle-to-PostgreSQL Migration
Story 10, from arXiv
A study evaluates whether a self-learning migration agent for Oracle-to-PostgreSQL improves over time, finding mixed results with significant memory leakage and limited policy adaptation.
Key points
- An evidence-graded migration agent showed a 0.015 improvement in reference conformity over a no-memory baseline.
- 264 out of 367 held-out units shared PostgreSQL references with training data, suggesting memory leakage.
- A model-free copy baseline outperformed all LLM systems in reference conformity.
- A human-gated fix cycle increased correct PostgreSQL units from 98/216 to 167/224.
- A write-back study showed a 54% decline in retrieval margin as the knowledge base grew.
- Six evaluation rules were derived to distinguish learning from leakage and artifacts.
Why it matters
Software engineers using AI for database migration should be cautious about relying on self-learning agents, as performance gains may stem from memory leakage rather than true learning.
Try this
Audit your migration agent's training data for reference leakage by comparing held-out units against training sources.
Counterpoint
The study highlights that many apparent improvements in migration agents may be due to reference leakage rather than genuine learning, undermining the reliability of self-learning systems.
Sources
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