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Found 11 papers, 9 papers with code
Automatically Testing Functional Properties of Code Translation Models
To this purpose, we develop the first property-guided search procedure for code translation models, where a model is repeatedly queried with slightly different parameters to produce alternative and potentially more correct translations.
Synthesizing a Progression of Subtasks for Block-Based Visual Programming Tasks
We propose a novel synthesis algorithm that generates a progression of subtasks that are high-quality, well-spaced in terms of their complexity, and solving this progression leads to solving the reference task.
Specifying and Testing $k$-Safety Properties for Machine-Learning Models
Here, we show the wide applicability of $k$-safety properties for machine-learning models and present the first specification language for expressing them.
Synthesizing Tasks for Block-based Programming
Our task synthesis algorithm operates by first mutating code $\rm C^{in}$ to obtain a set of codes $\{\rm C^{out}\}$.
Detecting Critical Bugs in SMT Solvers Using Blackbox Mutational Fuzzing
Formal methods use SMT solvers extensively for deciding formula satisfiability, for instance, in software verification, systematic test generation, and program synthesis.
Software Engineering
RAID: Randomized Adversarial-Input Detection for Neural Networks
In recent years, neural networks have become the default choice for image classification and many other learning tasks, even though they are vulnerable to so-called adversarial attacks.
Perfectly Parallel Fairness Certification of Neural Networks
Recently, there is growing concern that machine-learning models, which currently assist or even automate decision making, reproduce, and in the worst case reinforce, bias of the training data.
DeepSearch: A Simple and Effective Blackbox Attack for Deep Neural Networks
Although deep neural networks have been very successful in image-classification tasks, they are prone to adversarial attacks.
Targeted Greybox Fuzzing with Static Lookahead Analysis
In this paper, we introduce a novel technique for targeted greybox fuzzing using an online static analysis that guides the fuzzer toward a set of target locations, for instance, located in recently modified parts of the program.
Software Engineering Cryptography and Security
Harvey: A Greybox Fuzzer for Smart Contracts
First, Harvey extends standard greybox fuzzing with a method for predicting new inputs that are more likely to cover new paths or reveal vulnerabilities in smart contracts.
Software Engineering Cryptography and Security
Learning Inputs in Greybox Fuzzing
Greybox fuzzing is a lightweight testing approach that effectively detects bugs and security vulnerabilities.
Cryptography and Security Software Engineering
