Characterizing Real-World Bugs in Tile Programs for Automated Bug Detection

2026-05-19

AI Summary

This work presents the first systematic empirical study of code generation bugs in the Tile programming framework, which are often silent correctness or performance defects arising from tight coupling among input shapes, data types, and backend targets during multi-stage compilation—issues largely undetectable by existing testing tools. Through an in-depth analysis of 301 real-world bugs distilled from 401 GitHub reports, the study constructs the first comprehensive taxonomy of Tile-related defects, elucidating their root causes, triggering patterns, symptomatic manifestations, and effective repair strategies. The findings establish a foundational understanding that informs debugging, testing, and automated bug detection for Tile-based compiler infrastructures, offering both theoretical insights and practical guidance for improving their reliability and robustness.

Abstract

Tile-based programming frameworks are increasingly adopted to write high-performance GPU kernels in domains such as deep learning and scientific computing. While these frameworks enhance productivity and hardware utilization, their multi-stage compilation pipelines introduce distinct code generation bugs that are tightly coupled to input shapes, data types, and backend targets. These bugs often manifest as silent correctness or performance issues, making them difficult to detect using existing compiler testing tools. Additionally, the unique programming conventions of tile domain-specific languages complicate root cause identification, while fixing such bugs demands specialized knowledge of tile abstractions and compilation pipelines. Despite the growing adoption of tile-based systems, their code generation bugs remain largely unexplored. This paper presents the first systematic study of tile-program code generation bugs. We curate 401 bug reports from GitHub and identify 301 tile-program codegen bugs for analysis, categorizing the root causes, symptoms, input patterns, test oracles that trigger these bugs, and the strategies used to fix bugs. Our study provides foundational insights for building debugging, testing, and repair tools tailored to tile-based compiler infrastructures.

Problem

• tile programs
• code generation bugs
• GPU kernels
• compiler testing
• silent correctness issues

Innovation

• tile-based programming
• code generation bugs
• compiler testing
• systematic bug study
• GPU kernel compilation