ConcoLixir uses a reactive LLM oracle to improve line coverage in Python concolic testing by 8.6 to 17 percentage points on synthetic, real-world, and library targets.
Pawlowski, David L
6 Pith papers cite this work. Polarity classification is still indexing.
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Formalizes shared-context batched satisfiability and evaluates predicate-by-predicate, disjunctive over-approximation, and new Core-Literal Filter on symbolic abstraction and active property checking tasks.
A multi-layer uncertainty model finds that both transmission-layer and over-the-top exceptional-access architectures carry strictly higher modeled compromise risk than no-EA baselines, with risk distributions differing by architecture class under sparse evidence.
SAFT-GT is a new toolchain that bridges safety and security analysis for self-adaptive systems using Attack-Fault Tree generation and model combination, validated by a domain-expert user study.
A survey of LLMs for graph computation introduces a role-based taxonomy of executors versus planners and concludes that current models suit simple small-scale tasks but remain unreliable for large-scale exact computation.
A benchmarking experiment finds low rediscovery rates for three models on six Mythos-linked bug tasks, with only six target matches across 54 attempts under controlled prompting.
citing papers explorer
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ConcoLixir: Reactive LLM Discovery Oracles for Python Concolic Testing
ConcoLixir uses a reactive LLM oracle to improve line coverage in Python concolic testing by 8.6 to 17 percentage points on synthetic, real-world, and library targets.
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Shared-Context Batched Satisfiability
Formalizes shared-context batched satisfiability and evaluates predicate-by-predicate, disjunctive over-approximation, and new Core-Literal Filter on symbolic abstraction and active property checking tasks.
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Quantifying Compromise Risk in Exceptional Access Architectures Under Sparse and Indirect Evidence
A multi-layer uncertainty model finds that both transmission-layer and over-the-top exceptional-access architectures carry strictly higher modeled compromise risk than no-EA baselines, with risk distributions differing by architecture class under sparse evidence.
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Bridging Safety and Security in Complex Systems: A Model-Based Approach with SAFT-GT Toolchain
SAFT-GT is a new toolchain that bridges safety and security analysis for self-adaptive systems using Attack-Fault Tree generation and model combination, validated by a domain-expert user study.
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Are Large Language Models Suitable for Graph Computation? Progress and Prospects
A survey of LLMs for graph computation introduces a role-based taxonomy of executors versus planners and concludes that current models suit simple small-scale tasks but remain unreliable for large-scale exact computation.
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Benchmarking Mythos-Linked Bug Rediscovery
A benchmarking experiment finds low rediscovery rates for three models on six Mythos-linked bug tasks, with only six target matches across 54 attempts under controlled prompting.