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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In 45th IEEE/ACM International Conference on Software Engineering, ICSE 2023, Melbourne, Australia, May 14-20
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Stream of Revision adds action tokens to LLM decoding so the model can revise its own code history on the fly, cutting vulnerabilities in generated code with little added cost.
RepairAgent autonomously repairs 164 bugs on Defects4J including 39 not fixed by prior techniques by treating an LLM as an agent that invokes tools via a finite state machine and dynamic prompts.
EvoVuln evolves executable detection policies for five smart-contract vulnerability types using cold-start synthetic testing followed by few-shot refinement on five vulnerable and five safe contracts, reaching 71% macro F1 and enabling a small model to beat a large zero-shot model by 19 points at un
TBE identifies 32.1% of 92,011 equivalent surviving quantum mutants (29,536) via OpenQASM comparison after transpilation, reporting 100% precision and 82% accuracy on 348,299 mutants.
MEM-SBOM generates runtime SBOMs for Python applications by recovering modules, versions, and dependency graphs from volatile memory via Volatility 3 plugins, achieving 100% extraction accuracy on 51 apps.
Controlled corpus testing shows that fixed allclose oracles in LLM kernel benchmarks certify transcription-buggy kernels as correct while seeded fuzzing with fp64 references does not.
BioDefect is a new dataset for defect detection in bioinformatics software that improves average F1-scores by 29.61% to 38.04% over existing datasets when evaluated on nine language models.
DiffCodeGen clusters code candidates by behavioral similarity from fuzzing-synthesized inputs and selects the largest cluster's medoid, matching or exceeding prior test-time scaling methods with far less token and time cost.
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.
PBT-Bench is a new benchmark with 100 property-based testing problems across 40 Python libraries that measures LLM bug recall rates of 42.1-83.4% under guided prompting versus 31.4-76.7% in baseline.
A compositional algebraic decision diagram algorithm quantifies sensitivity in decision tree ensembles with certified error and confidence bounds, outperforming model counters on benchmarks.
Large-scale analysis of inactive GitHub repositories shows open source projects die primarily from insufficient value and ecosystem dynamics, not from pull request workflow problems, despite a common pattern of declining activity.
SMT-LLM builds a constraint graph from PyPI metadata and AST-derived imports, solves it with Z3, and uses LLM imputation only when needed, resolving 83.6% of HG2.9K snippets versus PLLM's 54.8% while cutting median time by 6.3x and LLM calls by 11x.
ConCovUp uses static analysis to ground LLM test generation and backward tracing to produce concurrent test drivers that raise average shared-memory access pair coverage from 36.6% to 68.1% on nine real-world libraries.
SmellBench is the first benchmark showing LLM agents resolve 47.7% of architectural code smells while accurately spotting false positives, but aggressive repairs often introduce new smells and degrade overall quality.
VulKey introduces hierarchical expert knowledge abstractions to guide LLMs in vulnerability repair, reporting 31.5% accuracy on PrimeVul (7.6% above best baseline) and strong results on Vul4J.
ClozeMaster masks bracketed structures in historical Rust bug code and uses LLMs to infill them, generating test programs that discovered 27 confirmed bugs in rustc and mrustc while outperforming existing fuzzers.
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.
QRisk isolates backend-specific abnormal error patterns on NISQ devices via delta debugging and mitigates them with commuting gate swaps, cutting excess noise by 24-45% on IBM backends where noise models predict no difference.
Clover fixes 96.8% of bugs on an RTL-repair benchmark using stochastic tree-of-thoughts and neural-symbolic agents, outperforming traditional and LLM baselines by 94% and 63% respectively with 87.5% pass@1.
LLM agents inject CWEs into student-authored code to generate personalized security examples; in a 71-student deployment, participants rated them more relevant than textbook cases but quantitative differences remained limited.
An LLM synthesizes an alias-free concurrency model (CIR) from natural language that is translated to a Petri net (CVN) for exhaustive verification and targeted repair, with goal-reachability checks to avoid incomplete fixes.
REAP automatically curates production-derived benchmarks for AI coding agents via LLM classification and stability checks, producing the Harvest benchmark with model solve rates of 42.9-58.2%.
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