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Found 5 papers, 2 papers with code
Evaluating Program Repair with Semantic-Preserving Transformations: A Naturalness Assessment
In this paper, we investigate the naturalness of semantic-preserving transformations and their impacts on the evaluation of NPR.
Symbol Correctness in Deep Neural Networks Containing Symbolic Layers
To handle AI tasks that combine perception and logical reasoning, recent work introduces Neurosymbolic Deep Neural Networks (NS-DNNs), which contain -- in addition to traditional neural layers -- symbolic layers: symbolic expressions (e. g., SAT formulas, logic programs) that are evaluated by symbolic solvers during inference.
State Selection Algorithms and Their Impact on The Performance of Stateful Network Protocol Fuzzing
In this work, we evaluate an extensive set of state selection algorithms on the same fuzzing platform that is AFLNet, a state-of-the-art fuzzer for network servers.
Legion: Best-First Concolic Testing
Legion incorporates a form of directed fuzzing that we call approximate path-preserving fuzzing (APPFuzzing) to investigate program states selected by MCTS.
Empirically Analyzing Ethereum's Gas Mechanism
Finally, our results suggest that under the current Gas cost model, nodes with modest computational resources are disadvantaged compared to their better resourced peers, which we identify as an ongoing threat to node diversity and network decentralization.
Cryptography and Security Performance
