Empirical analysis of 444 iOS apps using dynamic traffic interception found 282 leaking LLM API keys across ten providers, with only 28% remediation after three months.
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Bridging research and practice in simulation-based testing of industrial robot navigation systems
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Kops enables extension of the eBPF JIT with native operations using proof sequences checked by the existing verifier and native emits, validated by Lean 4 proofs, delivering up to 24% microbenchmark and 12% application speedups.
Incomplete constrainers in constrained decoding push LLMs into low-probability program regions, making unconstrained decoding outperform constrained decoding on functional correctness across seven models and three benchmarks.
KBSpec maintains an evolving knowledge base combining external docs and internal verifier feedback to improve LLM generation of verifiable JML specifications, achieving 10-25% higher verification pass rates.
Controlled ablation finds Popperian code-generation skill adds no separable correctness benefit over labels-only scaffold; gains track structure not content.
EvoRepair is the first experience-based self-evolving agent framework for automated vulnerability repair, reporting 90.46% overall success on PATCHEVAL and SEC-bench benchmarks.
SCARA introduces a four-stage pipeline using state-aware verification and constrained synthesis to remediate vulnerabilities in source-unavailable industrial software, reporting 100% precision and 88.9% success on a 15-case benchmark.
TDDev automates the full TDD loop for web app generation from requirements, delivering 34-48 percentage point quality gains and zero manual intervention in user studies.
A reusable framework generates verification instances with provably known robustness labels, revealing numeric tolerance issues and bugs in five verifiers while introducing difficulty profiles to diagnose failure modes.
Hydra enables asynchronous static error checking and targeted checkpoint-rollback repair during LLM code generation, cutting latency by up to 71% and token use by up to 70% versus post-hoc repair on C/C++ tasks.
MASPrism attributes failures in multi-agent systems by ranking candidates from prefill-stage NLL and attention signals of a 0.6B SLM, beating baselines by up to 33.41% Top-1 accuracy and proprietary LLMs by up to 89.5% relative improvement while processing traces in 2.66 seconds.
MultiLogBench shows that LLM performance on automated logging varies substantially across programming languages, demonstrating that single-language evidence is insufficient for general claims about model behavior or tool design.
LeetProof achieves higher rates of fully certified program synthesis from natural language by using a multi-modal verifier in Lean to validate specifications via randomized testing and delegate proofs to AI tools, outperforming single-mode baselines on benchmarks while uncovering defects in prior参考.
MR-Coupler leverages functional coupling analysis and LLMs to generate valid metamorphic test cases for over 90% of tasks while detecting 44% of real bugs, outperforming baselines by 64.90% in validity and 36.56% in false-alarm reduction.
A large-scale study of real-world repositories finds that AI-generated code differs from human-written code in complexity, structural traits, defect indicators, and commit-level activity patterns.
LLM-based security code review is vulnerable to framing bias, with a novel iterative refinement attack achieving 100% success in reintroducing vulnerabilities across real projects.
Large-scale analysis of 200K PyPI packages identifies 1,361 replicated popular packages, 256 replicated vulnerable packages, and 7 new replicated malicious packages, showing replication as a security threat vector.
PerGent, an agentic critique-refinement system for persona generation, reaches 96.9% expert approval in an industrial evaluation at Kinaxis and reproduces more pre-LLM expert content than single-shot baselines.
Three code-specific uncertainty axes (lexical, algorithmic, functional) yield an ensemble that raises average AUROC from 0.696 to 0.776 across five code LLMs, with one single-pass signal matching multi-pass baselines at lower cost.
dille detects silent semantic faults in random forest ML pipelines with 91% precision via data-informed static analysis on Kaggle notebooks, finding 12-18% of scripts affected.
Introduces ePCA framework using neural-symbolic isolation to force agents to formalize intentions as logical constraints, claiming zero attack success and false positive rates in tested scenarios.
T2J-Bench shows top coding agents achieve only 26.7-28.9% pass rate on codebase conversion under a three-stage observational equivalence check, with agents overestimating success by 66.6-97.8 points.
QUTest is a native OpenQASM testing framework that encodes Arrange/Act/Assert tests and 12 assertion types via pragma comments while remaining compatible with existing tools.
False-positive bug reports in the Linux kernel consume effort comparable to real bugs and can be filtered by LLMs using retrieval-augmented generation at 88% F1.
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Code Is More Than Text: Uncertainty Estimation for Code Generation
Three code-specific uncertainty axes (lexical, algorithmic, functional) yield an ensemble that raises average AUROC from 0.696 to 0.776 across five code LLMs, with one single-pass signal matching multi-pass baselines at lower cost.