reviews: 
Comments: - This paper propose a new approach, named WasmTest, that leverages LLMs to automate the generation of intermediate C++ code and test cases from high-level descriptions of Wasm functionalities.
- This paper includes a type-aware mutation strategy for mutation testing
- The evaluation results of this paper indicates the effectiveness of WasmTest through compile rate, code coverage, test correctness and bug detection rate
Strengths: - This paper is clearly written and easy to comprehend
- This paper tries to address an important and difficult problem, Wasm test generation
Weaknesses: Generally, I think this paper tries to address a typical software engineering task, automated test generation; however, it lacks a comprehensive empirical study about recent work, which needs to be mentioned and compared, and thus incorrectly claims that "effective testing methods remain underdeveloped" (Line 47).

Specifically, I have the following concerns:
- This paper overlook a large number of existing Wasm test generation approaches, e.g., WASMaker [1], wasm-smith [2], Wasm-mutate [3]
- The motivation of translating Wasm code into C++ code and then generate test cases is not clearly explained. Now that you utilize LLMs to comprehend the semantics of Wasm code, why not directly use LLMs to generate Wasm tests?
- This technical contribution of this paper is limited as each component in its approach has been proposed before. 1) Wasm has existing reverse engineering tool, wasm2c [5], so you need to explain why to use LLM for code translation and add comparison evaluation between your approach and wasm2c. 2) The step 3 in your approach (type-aware mutation). There exists a large portion of mutation testing approaches, even for c/c++ programs, e.g., CCmutator [4]. Additionally, the SOTA mutation testing approaches also include LLM, e.g., LEAM [6], which I suggest to consider.
- The evaluation section needs to be improved with more details about its setup. 1) Now that you leverage LLMs for code translation, you need further evaluation to show the semantic-preserving of your approach; 2) As for bug detection, you need to explain more details about your manually crafted bugs (Line 447), e.g., the fault types, the background of your involved engineers, which is important in the area of software engineering as this paper is an AI4SE one; 3).



These reference might be considered:

[1] Cao S, He N, She X, et al. WASMaker: Differential Testing of WebAssembly Runtimes via Semantic-Aware Binary Generation[C]//Proceedings of the 33rd ACM SIGSOFT International Symposium on Software Testing and Analysis. 2024: 1262-1273.

[2] Bytecode Alliance. 2023. Github wasm-tools repository. https://github.com/bytecodealliance/wasm-tools/tree/main/crates/wasm-smith

[3] Arteaga J C, Fitzgerald N, Monperrus M, et al. Wasm-mutate: Fuzzing WebAssembly compilers with e-graphs[C]//E-Graph Research, Applications, Practices, and Human-factors Symposium. 2022.

[4] Kusano M, Wang C. CCmutator: A mutation generator for concurrency constructs in multithreaded C/C++ applications[C]//2013 28th IEEE/ACM International Conference on Automated Software Engineering (ASE). IEEE, 2013: 722-725.

[5] https://github.com/WebAssembly/wabt/wasm2c

[6] Tian Z, Chen J, Zhu Q, et al. Learning to construct better mutation faults[C]//Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering. 2022: 1-13.
Recommendation: 3
Reviewer confidence: 5
Soundness/correctness: 2
Clarity: 3