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Sponsored AI Networking Cookbook: Practical recipes for AI-assisted network automation and development AI Networking Cookbook: Practical recipes for AI-assisted network automation and development

Author

Junda Zhao

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Jul 2026

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 generation pipelines, and remains applicable to both buggy and bug-free code.

Junda Zhao, Shurui Zhou, Eldan Cohen · 0 citations
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 already be buggy and the goal is to expose the bug within the code-under-test, they no longer serve as reliable indicators.

Junda Zhao, Shurui Zhou, Eldan Cohen · 0 citations

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