AIFaultBench: A Reproducible Benchmark of Real-World Faults in AI Software Systems

Mehil B Shah, Mohammad Masudur Rahman, Foutse Khomh

AI-based software systems remain difficult to debug because their failures can involve interactions among code, models, data, dependencies, hardware, and execution environments. Existing benchmarks cover specific classes of AI systems, while concerns about fault realism and reproducibility highlight the need for documented execution conditions and evidence. We present AIFaultBench, a benchmark of 770 real-world bugs from 105 AI-based software repositories across 76 organizations. It covers six classes of AI systems: machine learning, deep learning, reinforcement learning, systems based on large language models, agentic AI, and AI tooling and infrastructure. Each bug record connects the original report and affected codebase to structured metadata, environment setup scripts, a reproduction trigger, and investigation evidence. We reproduced 652 bugs (84.7%) under the recorded execution conditions and documented the outcomes of the remaining 118 attempts. Through its broad coverage, documented reproduction evidence, and common structure, AIFaultBench supports empirical studies of AI faults, debugging tool evaluation, practical failure investigation, and teaching with real-world…