arXiv AI
Beyond Rule-Based Mutation Testing: Test-Aware Mutant Generation Using Large Language Models
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Do Coverage and Mutation Scores of LLM-Generated Test Suites Correlate with Their Effectiveness? (Replicability Study)
arXiv:2607. 22880v1 Announce Type: cross Abstract: Recent advances in large language models (LLMs) have driven growing interest in using LLMs to automate test generation.
arXiv AI
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Beyond Fail-to-Pass: Iterative Hardening of Co-Generated Bug Reproduction Tests and Fixes
arXiv:2607. 19843v1 Announce Type: cross Abstract: Large language models (LLMs) have made automated program repair (APR) increasingly practical for real-world bugs, but repairing directly from bug reports remains underconstrained.
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SWE-Test: Benchmarking LLM Vulnerability Discovery via Input Prediction
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Evaluating and Mitigating the Misguidance Effect of Buggy Code in LLM-Generated Unit Tests
arXiv:2607. 22883v1 Announce Type: cross Abstract: While Large Language Models (LLMs) show great promise for automating unit test generation, recent studies suggest that the quality of generated tests can be negatively impacted when models are prompted with buggy code.
arXiv AI
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SyzHarness: Patch-Based Kernel Bug Reproduction with LLM-Synthesized Fuzzing Harnesses
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Hugging Face Trending Papers
Jul 24
Evaluating and Mitigating the Misguidance Effect of Buggy Code in LLM-Generated Unit Tests
While Large Language Models (LLMs) show great promise for automating unit test generation, recent studies suggest that the quality of generated tests can be negatively impacted when models are prompted with buggy code. This paper presents a new metric to quantitatively measure the "misguidance effect," a phenomenon where buggy code steers LLMs toward generating tests that validate its erroneous behavior rather than expose it.