LLM-based Java program repair models lose over 50% of their bug-fixing success rate when presented with equivalent but syntactically varied buggy code.
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LLMs show up to 60.58% social bias in generated code; a new Fairness Monitor Agent cuts bias by 65.1% and raises functional correctness from 75.80% to 83.97%.
A large-scale study finds that many LLM code translation failures are false negatives due to improper evaluation configurations rather than incorrect translations.
citing papers explorer
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HEJ-Robust: A Robustness Benchmark for LLM-Based Automated Program Repair
LLM-based Java program repair models lose over 50% of their bug-fixing success rate when presented with equivalent but syntactically varied buggy code.
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Social Bias in LLM-Generated Code: Benchmark and Mitigation
LLMs show up to 60.58% social bias in generated code; a new Fairness Monitor Agent cuts bias by 65.1% and raises functional correctness from 75.80% to 83.97%.
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Beyond Translation Accuracy: Addressing False Failures in LLM-Based Code Translation
A large-scale study finds that many LLM code translation failures are false negatives due to improper evaluation configurations rather than incorrect translations.