Deployment Risk Assessment Using Diff-Aware Features: A Case Study at Prime Video

2026-07-01 · Source: cs.SE updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering, Data Science & Analytics · Depth: Advanced, extended

Summary

Amazon Prime Video has developed a diff-aware framework for assessing code deployment risk, aiming to reduce reliance on blanket deployment freezes during critical live events. This framework extracts features directly from code modifications, avoiding developer-specific metadata or extensive historical data. It employs LLMs, specifically Claude Sonnet 3.7, as multi-language feature extractors for quantitative metrics like code complexity and change volume, and static analyzers for qualitative indicators such as coding style violations. Evaluated on Prime Video's production environment (2,980 commits) and the public ApacheJIT dataset (22,300 commits), the best-performing XGBoost model achieved an average recall of 0.83 and F1 score of 0.81. Ablation studies revealed that structural code complexity is a substantially stronger risk signal than change-level volume metrics, which were found to be noisy predictors.

Key takeaway

For MLOps Engineers managing deployments during critical live events, you should implement diff-aware risk assessment to reduce blanket freezes. Your systems can use LLMs for multi-language feature extraction, focusing on code complexity over raw change volume for stronger risk signals. This approach enables more targeted reviews, potentially clearing most safe changes while flagging genuinely risky ones, thereby accelerating feature velocity without compromising reliability.

Key insights

Diff-aware features, extracted via LLMs, enable effective, privacy-preserving code change risk assessment without historical or developer metadata.

Principles

• Code complexity predicts risk better than change volume.
• LLMs can extract multi-language code features.
• Prioritize recall for human-in-the-loop guardrails.

Method

The framework extracts 28 diff-aware features (quantitative code/change-level, qualitative style violations) using LLMs for multi-language metrics and static analyzers for style. ML classifiers (XGBoost, Random Forest) then predict change risk, optimized for F1-score with a 0.2 threshold.

In practice

• Use LLMs for multi-language feature extraction.
• Focus on code complexity metrics for risk prediction.
• Implement a human-in-the-loop guardrail system.

Topics

• Deployment Risk Assessment
• Diff-Aware Features
• Large Language Models
• Code Complexity Metrics
• Just-in-Time Defect Prediction
• Prime Video Operations

Best for: CTO, VP of Engineering/Data, AI Architect, MLOps Engineer, Machine Learning Engineer, Director of AI/ML

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.SE updates on arXiv.org.