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    <abstract>Test case synthesis is crucial for evaluating and ranking programs generated by large language models (LLMs). However, constructing high-quality test cases remains challenging because reliable expected outputs are often difficult to obtain. We propose Confidence-Gated Transductive Test Generation (CoTT), which first uses an efficient inductive procedure and invokes transductive generation only when inductive confidence is low. This adaptive design improves output reliability while allocating extra computation only when needed. On code reranking benchmarks, CoTT outperforms prior baselines across the reported metrics while reducing cost relative to applying transductive generation to every input. These results show that confidence-based allocation of test-time computation provides a favorable efficiency-effectiveness trade-off with a single efficient LLM.</abstract>
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%0 Conference Proceedings
%T Confidence-Gated Transductive Test Generation for Code Reranking
%A Lee, Sungjae
%A Yoon, Youngsik
%A Song, Seockbean
%A Wang, Siwei
%A Chen, Wei
%A Ok, Jungseul
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F lee-etal-2026-confidence
%X Test case synthesis is crucial for evaluating and ranking programs generated by large language models (LLMs). However, constructing high-quality test cases remains challenging because reliable expected outputs are often difficult to obtain. We propose Confidence-Gated Transductive Test Generation (CoTT), which first uses an efficient inductive procedure and invokes transductive generation only when inductive confidence is low. This adaptive design improves output reliability while allocating extra computation only when needed. On code reranking benchmarks, CoTT outperforms prior baselines across the reported metrics while reducing cost relative to applying transductive generation to every input. These results show that confidence-based allocation of test-time computation provides a favorable efficiency-effectiveness trade-off with a single efficient LLM.
%U https://aclanthology.org/2026.findings-emnlp.1715/
%P 36648-36660

Markdown (Informal)

[Confidence-Gated Transductive Test Generation for Code Reranking](https://aclanthology.org/2026.findings-emnlp.1715/) (Lee et al., Findings 2026)

• Confidence-Gated Transductive Test Generation for Code Reranking (Lee et al., Findings 2026)

ACL

• Sungjae Lee, Youngsik Yoon, Seockbean Song, Siwei Wang, Wei Chen, and Jungseul Ok. 2026. Confidence-Gated Transductive Test Generation for Code Reranking. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 36648–36660, Budapest, Hungary. Association for Computational Linguistics.