基于跨框架差分模糊测试的深度学习库API测试
Testing Deep Learning Library APIs via Cross-Framework Differential Fuzzing
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中文总结 AI 辅助
本文提出跨框架差分模糊测试方法Xamt，用于测试7个深度学习库的API，构建676个经验证的API组，识别出72个差异案例，其中25份已被开发者确认并修复。
中文摘要 AI 辅助
深度学习库是许多安全和可靠性关键应用的基础，但现有的API级测试技术往往依赖库内属性或CPU-GPU差分预言机，可能遗漏跨硬件后端表现一致的缺陷。本文提出Xamt，一种针对深度学习库API的跨框架差分模糊测试方法。Xamt构建并测试了7个库中旨在实现等效操作的经执行验证的API组，它利用显式API别名和参数角色归一化来构建候选对应关系，并通过成对执行和针对规范普通输入的组级行为检查来验证这些对应关系。所得组采用方差引导的差分模糊测试进行探索，输入包括普通、边界和非有限输入。崩溃和不一致预言机标记出表现出异常终止或输出不一致的执行，以便后续复现和分析。在7个库中，Xamt构建了包含2563个匹配API的676个经执行验证的组，其中识别出72个可独立复现的差异案例，包括4个崩溃案例和68个输出不一致案例；在72份开发者报告中，25份已被确认，其中23份已修复。
英文摘要
Deep learning libraries underpin many safety- and reliability-critical applications, yet existing API-level testing techniques often rely on intra-library properties or CPU--GPU differential oracles and may miss defects that behave consistently across hardware backends. We present Xamt, a cross-framework differential fuzzing approach for deep learning library APIs. Xamt constructs and tests execution-validated groups of APIs intended to implement equivalent operations across seven libraries. It uses explicit API aliases and parameter-role normalization to construct candidate correspondences and validates them through pairwise execution and a group-level behavioral check on canonical ordinary inputs. The resulting groups are explored using variance-guided differential fuzzing with ordinary, boundary, and non-finite inputs. Crash and inconsistency oracles flag executions exhibiting abnormal termination or inconsistent outputs for subsequent reproduction and analysis. Across the seven libraries, Xamt constructs 676 execution-validated groups containing 2,563 matched APIs. Among these, Xamt identifies 72 independently reproduced discrepancy cases, including 4 crash cases and 68 output inconsistencies. Among the 72 developer reports, 25 have been confirmed, including 23 that have been fixed.
