一句话：提出 WarpL，通过变异推断定位 WebAssembly 运行时中导致性能问题的次优指令序列。
WebAssembly 运行时的性能问题会削弱托管服务能力，而许多问题源于运行时的编译器对输入 Wasm 程序编译不够优化；面向应用层低效的现有性能调试方法难以定位这类问题。本文提出 WarpL，一种基于变异的推断方法：先获得功能相似但不再表现出该性能问题的变异程序，再对比原始程序与变异程序的机器码，从而隔离出导致性能问题的精确次优指令序列，缩小根因范围。作者将 WarpL 实现为开源工具，在三个广泛使用的 Wasm 运行时中的 12 个真实性能问题上进行评估，成功定位 10 个问题的精确原因，并诊断出 Wasmtime 中 6 个此前未知的性能问题。
Performance debugging in WebAssembly (Wasm) runtimes is essential for ensuring the robustness of Wasm, especially since performance issues have frequently occurred in Wasm runtimes, which can significantly degrade the capabilities of hosted services. Many performance issues in Wasm runtimes result from suboptimal compilation of input Wasm programs, for which existing performance debugging methods primarily designed for application-level inefficiencies are not well-suited. In this paper, we present WarpL, a novel mutation-based approach that aims to identify the exact suboptimal instruction sequences responsible for the performance issues in Wasm runtimes, thereby narrowing down the root causes. Specifically, WarpL obtains a functionally similar mutant in which the performance issue does not manifest, and isolates the exact suboptimal instructions by comparing the machine code of the original and mutated programs. We implement WarpL as an open-source tool and evaluate it on 12 real-world performance issues across three widely used Wasm runtimes. WarpL identified the exact causes in 10 out of 12 issues. Notably, we have used WarpL to successfully diagnose six previously unknown performance issues in Wasmtime.
@inproceedings{abs-2604-13693,
title = {Debugging Performance Issues in WebAssembly Runtimes via Mutation-based Inference},
author = {Ruiying Zeng and Shuyao Jiang and Wenxuan Zhao and Yangfan Zhou},
booktitle = {ICSE},
year = {2026},
doi = {10.1145/3744916.3773141}
}