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Found 7 papers, 4 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.
Importance-Driven Deep Learning System Testing
Deep Learning (DL) systems are key enablers for engineering intelligent applications due to their ability to solve complex tasks such as image recognition and machine translation.
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.
DeepSmartFuzzer: Reward Guided Test Generation For Deep Learning
Among those, CGF aims to produce new test inputs by fuzzing existing ones to achieve high coverage on a test adequacy criterion (i. e. coverage criterion).
DeepFault: Fault Localization for Deep Neural Networks
Deep Neural Networks (DNNs) are increasingly deployed in safety-critical applications including autonomous vehicles and medical diagnostics.
