Finding Compiler-Platform Interaction Bugs in Deep Learning Pipelines via Cross-Layer Constraints

2026-06-18 · Source: cs.SE updates on arXiv.org · Field: Technology & Digital — Software Development & Engineering, Artificial Intelligence & Machine Learning · Depth: Expert, short

Summary

XCheck, an automated deep learning (DL) compiler testing framework, addresses the challenge of compiler-platform interaction bugs in DL pipelines. Submitted on June 16, 2026, this framework moves beyond traditional type constraint-based testing by deriving and prioritizing "full-stack constraints" that guide model generation and characterize compilation behaviors. It specifically targets bugs arising from violated assumptions across compilation passes and hardware platforms, which existing methods often overlook. XCheck also enables behavior equivalence partitioning by inserting assertions to monitor distinct compilation symptoms. Evaluated on three widely-used DL compilers, XCheck identified 2,034 bug-revealing cases, including memory overflows, integer overflows, and silent unexpected compilations, all rooted in these complex compiler-platform interactions.

Key takeaway

For Machine Learning Engineers or compiler developers building or deploying deep learning models, traditional testing methods often miss critical compiler-platform interaction bugs. You should adopt testing frameworks like XCheck that utilize full-stack constraints to uncover issues such as memory or integer overflows. This approach ensures more robust model deployment by proactively identifying subtle, interaction-sensitive compilation behaviors that impact reliability and performance across diverse hardware.

Key insights

Compiler-platform interaction bugs in DL pipelines are best found by deriving and prioritizing full-stack constraints.

Principles

• Correctness depends on implicit constraints across the compilation stack.
• Interaction bugs stem from violated assumptions across compilation passes and hardware.
• Prioritize constraints exposing interaction-sensitive behaviors.

Method

XCheck extracts full-stack constraints, prioritizes interaction-sensitive behaviors for model generation, and inserts assertions for behavior equivalence partitioning.

In practice

• Use full-stack constraints to guide DL model generation for testing.
• Implement assertions to monitor distinct compilation symptoms.

Topics

• Deep Learning Compilers
• Compiler Testing
• Bug Detection
• Full-Stack Constraints
• Software Engineering
• AI Pipelines

Best for: AI Engineer, Research Scientist, AI Scientist, Software Engineer, Machine Learning Engineer

Related on AIssential

• Between Scripts and Frameworks: Escaping the Complexity Trap in ML Pipelines — Complexity, not framework absence, is the primary pain point in growing ML projects.
• Monitoring LLM behavior: Drift, retries, and refusal patterns — Robust AI evaluation requires a two-layered stack combining deterministic and model-based assertions within offline and online pipelines.
• AI Code Review Only Catches Half of Your Bugs — AI code review needs explicit requirements and intent to catch design flaws and security vulnerabilities.
• BugBash'26 Morning of Day 1 — Peter Alvaro's 20-year journey in distributed systems reveals no silver bullet, but continuous progress through diverse, evolving approaches.
• AttackPathGNN: Cross-function vulnerability detection in smart contracts using state interference graphs and conjunction pooling — Cross-function smart contract vulnerabilities require reasoning over inter-function relationships, not just single-function patterns.
• MORTAR: Multi-turn Metamorphic Testing for LLM-based Dialogue Systems — MORTAR offers a bias-free, cost-effective metamorphic testing approach for multi-turn LLM dialogue systems.

Open in AIssential →

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