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Eldan Cohen

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Evaluating and Mitigating the Misguidance Effect of Buggy Code in LLM-Generated Unit Tests

A specification-based unit test generation paradigm that replaces the code under test in the prompt with an LLM-generated specification docstring is introduced, showing that this paradigm effectively reduces misguided tests while substantially increasing effective tests, improves multi-round, feedback-driven test gener...

Junda Zhao, Shurui Zhou, Eldan Cohen · 1 citation
Jul 2026

Do Coverage and Mutation Scores of LLM-Generated Test Suites Correlate with Their Effectiveness? (Replicability Study)

It is found that the usefulness of coverage and mutation is highly context-dependent: in regression-style settings where the code provided to the LLM can be reasonably assumed bug-free, these metrics can provide meaningful signals when comparing across models; in another common scenario where the code-under-test may al...

Junda Zhao, Shurui Zhou, Eldan Cohen · 0 citations
Conference Open access 2026

Lightweight and Faithful Visual Condition Checking in Behavior Trees via Expert-Regularized Reinforcement Learning

CERL (Condition-node Expert-regularized Reinforcement Learning), a framework that leverages expert-regularized reinforcement learning to preserve semantic faithfulness, while employing a factorized policy that aggregates sequential condition-node decisions into a single decision unit to alleviate credit assignment chal...

Hyosik Moon, Eldan Cohen · 0 citations

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