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Ruiying Zeng

dblp:357/3589 · DBLP profile ↗
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3ranked-venue papers
0as first author
3since 2021 · last 2025
0009-0000-6367-0077ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Software engineering, systems software and programming languages · 3 · 3 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Software engineering, system software, and programming languages
2 papers
Software testing · 76% Runtime systems and virtual machines · 24%
Computer architecture, parallel and distributed computing, and storage systems
1 paper
Performance modeling and evaluation · 100%

Topics — the 6 heaviest of 6, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Runtime systems and virtual machines › language runtime
webassembly runtime
0.922024
Revealing Performance Issues in Server-Side WebAssembly Runtimes Via Differential Testing · ASE 2023
Wapplique: Testing WebAssembly Runtime via Execution Context-Aware Bytecode Mutation · ISSTA 2024
Software testing
fuzzing
0.812024
Wapplique: Testing WebAssembly Runtime via Execution Context-Aware Bytecode Mutation · ISSTA 2024
Software testing › test execution › runtime testing
webassembly runtime testing
0.812024
Wapplique: Testing WebAssembly Runtime via Execution Context-Aware Bytecode Mutation · ISSTA 2024
Software testing
differential testing
0.712023
Revealing Performance Issues in Server-Side WebAssembly Runtimes Via Differential Testing · ASE 2023
Software testing
performance issue detection
0.712023
Revealing Performance Issues in Server-Side WebAssembly Runtimes Via Differential Testing · ASE 2023
Performance modeling and evaluation
performance diagnosis
0.212023
Revealing Performance Issues in Server-Side WebAssembly Runtimes Via Differential Testing · ASE 2023

Methods — techniques the papers use, named apart from their topics

execution time ratio oracle · 1.3differential testing · 1.3code-fragment substitution · 0.8bytecode mutation · 0.8
YearPublicationVenuePosition
2025 Distinguishability-Guided Test Program Generation for WebAssembly Runtime Performance Testing
abstract
WebAssembly (Wasm) is a binary instruction format designed as a portable compilation target, which has been widely used on both the web and server sides in recent years. As high performance is a critical design goal of Wasm, it is essential to conduct performance testing for Wasm runtimes. However, existing research on Wasm runtime performance testing still suffers from insufficient high-quality test programs. To solve this problem, we propose a novel test program generation approach WarpGen. It first extracts code snippets from historical issue-triggering test programs as initial operators, then inserts an operator into a seed program to synthesize a new test program. To verify the quality of generated programs, we propose an indicator called distinguishability, which refers to the ability of a test program to distinguish abnormal performance of specific Wasm runtimes. We apply WarpGen for performance testing on four Wasm runtimes and verify its effectiveness compared with baseline approaches. In particular, WarpGen has identified seven new performance issues in three Wasm runtimes.
Shuyao Jiang, Ruiying Zeng, Yangfan Zhou 0002, Michael R. Lyu
SANER2
2024 Wapplique: Testing WebAssembly Runtime via Execution Context-Aware Bytecode Mutation
abstract
Reliability is the top concern to runtimes. This paper studies how to test Wasm runtime, by presenting Wapplique, the first Wasm bytecode mutation-based fuzzing tool. Wapplique solves the diversity/efficiency dilemma in generating test cases with a specifically-tailored code-fragment substitution approach for Wasm. In particular, Wapplique appliqués code fragments from real-world programs to seed programs to enhance the diversity of the seeds. Via sophisticated code analysis algorithms we design, Wapplique also guarantees the validity of the resulting programs. This allows Wapplique to generate tremendous valid and diverse Wasm programs as test cases to well exercise target runtimes. Our experiences on applying Wapplique in testing four prevalent real-world runtimes indicate that it can generate test cases efficiently, achieve high coverage, and find 20 previously unknown bugs.
Ruiying Zeng, Yangfan Zhou 0002
ISSTA2
2023 Revealing Performance Issues in Server-Side WebAssembly Runtimes Via Differential Testing
abstract
WebAssembly (Wasm) is a bytecode format originally serving as a compilation target for Web applications. It has recently been used increasingly on the server side, e.g., providing a safer, faster, and more portable alternative to Linux containers. With the popularity of server-side Wasm applications, it is essential to study performance issues (i.e., abnormal latency) in Wasm runtimes, as they may cause a significant impact on server-side applications. However, there is still a lack of attention to performance issues in server-side Wasm runtimes. In this paper, we design a novel differential testing approach WarpDiff to identify performance issues in server-side Wasm runtimes. The key insight is that in normal cases, the execution time of the same test case on different Wasm runtimes should follow an oracle ratio. We identify abnormal cases where the execution time ratio significantly deviates from the oracle ratio and subsequently locate the Wasm runtimes that cause the performance issues. We apply WarpDiff to test five popular server-side Wasm runtimes using 123 test cases from the LLVM test suite and demonstrate the top 10 abnormal cases we identified. We further conduct an in-depth analysis of these abnormal cases and summarize seven performance issues, all of which have been confirmed by the developers. We hope our work can inspire future investigation on improving Wasm runtime implementation and thus promoting the development of server-side Wasm applications.
Shuyao Jiang, Ruiying Zeng, Zihao Rao, Jiazhen Gu, Yangfan Zhou 0002, Michael R. Lyu
ASE2