DuET: Dual Execution for Test Output Prediction with Generated Code and Pseudocode
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 sufferi...
| Authors | Hojae Han, Jaejin Kim, Seung-won Hwang et al. |
|---|---|
| Primary category | cs.SE |
| Published | 13 April 2026 |
| Last updated at source | 13 April 2026 |
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