The paper presents OverEager-Gen, a 500-scenario benchmark showing that removing consent declarations from prompts increases overeager actions by 11.9-17.2 percentage points across models, with agent framework choice dominating base-model effects.
An analysis and survey of the development of mutation test- ing
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LLM code modernizers produce semantic drift in 39.7% of legacy-Python-2 cases and endorse 31.7% of those drifts in self-review, with rates varying widely across models but not tracking capability.
Noise from quantum hardware simulators significantly alters mutant detection distances, making equivalent mutants harder to separate from faults, with output-distribution metrics reaching 73.03% accuracy and 74.89% F1-score under device-specific thresholds.
Quantum circuits show high average condition (97.56%) and decision (97.63%) coverage but lower path coverage (71.84%), with probabilistic versions adding confidence levels (averages 88.87%, 88.65%, 37.18%); mutation testing reveals weak or no correlation between structural coverage and fault finding
A dual-axis quality framework ranks DL mutation operators by statistical resistance and Jaccard-based realism to real faults, enabling up to 55.6% fewer mutants on held-out validation data without dropping baseline performance.
QuanForge introduces statistical mutation killing and nine post-training mutation operators for QNNs to distinguish test suites and localize vulnerable circuit regions.
MutDafny uses 40 mutation operators on 794 real-world Dafny programs to detect weak specifications, manually confirming five such cases at a rate of one per 241 lines.
Neural Change Prediction generates mutation data to train bidirectional models linking code changes to behavioral effects for any executable program.
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