Search Results for author:
Found 11 papers, 6 papers with code
Bugs in Large Language Models Generated Code: An Empirical Study
The bug patterns are presented in the form of a taxonomy.
GIST: Generated Inputs Sets Transferability in Deep Learning
As the demand for verifiability and testability of neural networks continues to rise, an increasing number of methods for generating test sets are being developed.
Reinforcement learning informed evolutionary search for autonomous systems testing
Evolutionary search-based techniques are commonly used for testing autonomous robotic systems.
AmbieGen: A Search-based Framework for Autonomous Systems Testing
To address this challenge, we introduce AmbieGen, a search-based test case generation framework for autonomous systems.
A Probabilistic Framework for Mutation Testing in Deep Neural Networks
Methods: In this work, we propose a Probabilistic Mutation Testing (PMT) approach that alleviates the inconsistency problem and allows for a more consistent decision on whether a mutant is killed or not.
A Search-Based Framework for Automatic Generation of Testing Environments for Cyber-Physical Systems
We compared three configurations of AmbieGen: based on a single objective genetic algorithm, multi objective, and random search.
Machine Learning Application Development: Practitioners' Insights
In this paper, we report about a survey that aimed to understand the challenges and best practices of ML application development.
Silent Bugs in Deep Learning Frameworks: An Empirical Study of Keras and TensorFlow
This paper presents the first empirical study of Keras and TensorFlow silent bugs, and their impact on users' programs.
Models of Computational Profiles to Study the Likelihood of DNN Metamorphic Test Cases
We investigate the distribution of computational profile likelihood of metamorphic test cases with respect to the likelihood distributions of training, test and error control cases.
Data Driven Testing of Cyber Physical Systems
The most common approach for pre-deployment testing is to model the system and run simulations with models or software in the loop.
Documentation of Machine Learning Software
We are interested in understanding the nature and triggers of the problems and the impact of the users' levels of expertise in the process of documentation evolution.
