@inproceedings{han-etal-2026-duet,
title = "{D}u{ET}: Dual Execution for Test Output Prediction with Generated Code and Pseudocode",
author = "Han, Hojae and
Kim, Jaejin and
Hwang, Seung-won and
Kim, Yu Jin and
Lee, Moontae",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.2144/",
pages = "43221--43243",
ISBN = "979-8-89176-395-1",
abstract = "This work addresses test output prediction, a key challenge in test case generation. To improve the reliability of predicted outputs by LLMs, prior approaches generate code first to ground predictions. One grounding strategy is direct execution of generated code, but even minor errors can cause failures. To address this, we introduce LLM-based pseudocode execution, which grounds prediction on more error-resilient pseudocode and simulates execution via LLM reasoning. We further propose DUET, a dual-execution framework that combines both approaches by functional majority voting. Our analysis shows the two approaches are complementary in overcoming the limitations of direct execution suffering from code errors, and pseudocode reasoning from hallucination. On LiveCodeBench, DUET achieves the state-of-the-art performance, improving Pass@1 by 13.6 pp. For filtering candidates in code generation, DUET shows the best Pass@1 on LiveCodeBenchEasy, BigCodeBench-Hard, DevEval and HumanEval(+)."
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<abstract>This work addresses test output prediction, a key challenge in test case generation. To improve the reliability of predicted outputs by LLMs, prior approaches generate code first to ground predictions. One grounding strategy is direct execution of generated code, but even minor errors can cause failures. To address this, we introduce LLM-based pseudocode execution, which grounds prediction on more error-resilient pseudocode and simulates execution via LLM reasoning. We further propose DUET, a dual-execution framework that combines both approaches by functional majority voting. Our analysis shows the two approaches are complementary in overcoming the limitations of direct execution suffering from code errors, and pseudocode reasoning from hallucination. On LiveCodeBench, DUET achieves the state-of-the-art performance, improving Pass@1 by 13.6 pp. For filtering candidates in code generation, DUET shows the best Pass@1 on LiveCodeBenchEasy, BigCodeBench-Hard, DevEval and HumanEval(+).</abstract>
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%0 Conference Proceedings
%T DuET: Dual Execution for Test Output Prediction with Generated Code and Pseudocode
%A Han, Hojae
%A Kim, Jaejin
%A Hwang, Seung-won
%A Kim, Yu Jin
%A Lee, Moontae
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F han-etal-2026-duet
%X This work addresses test output prediction, a key challenge in test case generation. To improve the reliability of predicted outputs by LLMs, prior approaches generate code first to ground predictions. One grounding strategy is direct execution of generated code, but even minor errors can cause failures. To address this, we introduce LLM-based pseudocode execution, which grounds prediction on more error-resilient pseudocode and simulates execution via LLM reasoning. We further propose DUET, a dual-execution framework that combines both approaches by functional majority voting. Our analysis shows the two approaches are complementary in overcoming the limitations of direct execution suffering from code errors, and pseudocode reasoning from hallucination. On LiveCodeBench, DUET achieves the state-of-the-art performance, improving Pass@1 by 13.6 pp. For filtering candidates in code generation, DUET shows the best Pass@1 on LiveCodeBenchEasy, BigCodeBench-Hard, DevEval and HumanEval(+).
%U https://aclanthology.org/2026.findings-acl.2144/
%P 43221-43243
Markdown (Informal)
[DuET: Dual Execution for Test Output Prediction with Generated Code and Pseudocode](https://aclanthology.org/2026.findings-acl.2144/) (Han et al., Findings 2026)
ACL