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Found 22 papers, 4 papers with code
Evaluation of Out-of-Distribution Detection Performance on Autonomous Driving Datasets
Safety measures need to be systemically investigated to what extent they evaluate the intended performance of Deep Neural Networks (DNNs) for critical applications.
Automotive Perception Software Development: An Empirical Investigation into Data, Annotation, and Ecosystem Challenges
An industry of data and annotation services has emerged to serve the development of such data-intensive automotive software components.
Automotive Multilingual Fault Diagnosis
Automated fault diagnosis can facilitate diagnostics assistance, speedier troubleshooting, and better-organised logistics.
Performance Analysis of Out-of-Distribution Detection on Trained Neural Networks
Implementing Deep Neural Networks (DNN) for non-safety related applications have shown remarkable achievements over the past years; however, for using DNNs in safety critical applications, we are missing approaches for verifying the robustness of such models.
Ergo, SMIRK is Safe: A Safety Case for a Machine Learning Component in a Pedestrian Automatic Emergency Brake System
We initiated a research project with the goal to demonstrate a complete safety case for an ML component in an open automotive system.
Exploring ML testing in practice -- Lessons learned from an interactive rapid review with Axis Communications
Four researchers from Lund University and RISE Research Institutes and four practitioners from Axis Communications reviewed a set of 180 primary studies on ML testing.
Quality Assurance of Generative Dialog Models in an Evolving Conversational Agent Used for Swedish Language Practice
Due to the migration megatrend, efficient and effective second-language acquisition is vital.
Machine Learning Testing in an ADAS Case Study Using Simulation-Integrated Bio-Inspired Search-Based Testing
This paper presents an extended version of Deeper, a search-based simulation-integrated test solution that generates failure-revealing test scenarios for testing a deep neural network-based lane-keeping system.
Agility in Software 2.0 -- Notebook Interfaces and MLOps with Buttresses and Rebars
Artificial intelligence through machine learning is increasingly used in the digital society.
Machine Learning-Assisted Analysis of Small Angle X-ray Scattering
Small angle X-ray scattering (SAXS) is extensively used in materials science as a way of examining nanostructures.
Efficient and Effective Generation of Test Cases for Pedestrian Detection -- Search-based Software Testing of Baidu Apollo in SVL
This paper presents the results of our proposed test generation technique in the 2021 IEEE Autonomous Driving AI Test Challenge.
Performance Analysis of Out-of-Distribution Detection on Various Trained Neural Networks
Understanding the relationship between training results and supervisor performance is valuable to improve robustness of the model and indicates where more work has to be done to create generalized models for safety critical applications.
Exploring the Assessment List for Trustworthy AI in the Context of Advanced Driver-Assistance Systems
Moreover, bigger questions related to societal and environmental impact cannot be tackled by an ADAS supplier in isolation.
Test Automation with Grad-CAM Heatmaps -- A Future Pipe Segment in MLOps for Vision AI?
In this paper, we demonstrate how Grad-CAM heatmaps can be used to increase the explainability of an image recognition model trained for a pedestrian underpass.
Digital Twins Are Not Monozygotic -- Cross-Replicating ADAS Testing in Two Industry-Grade Automotive Simulators
Based on a minimalistic scene, we compare critical test scenarios generated using our SBST solution in these two simulators.
The AIQ Meta-Testbed: Pragmatically Bridging Academic AI Testing and Industrial Q Needs
In this paper, we share our working definition and a pragmatic approach to address the corresponding quality assurance with a focus on testing.
Enabling Image Recognition on Constrained Devices Using Neural Network Pruning and a CycleGAN
One promising approach for smart cameras is edge AI, i. e., deploying AI technology on IoT devices.
An Autonomous Performance Testing Framework using Self-Adaptive Fuzzy Reinforcement Learning
On the other hand, if the optimal performance testing policy for the intended objective in a testing process instead could be learned by the testing system, then test automation without advanced performance models could be possible.
Requirements Engineering for Machine Learning: Perspectives from Data Scientists
We conclude that development of ML systems demands requirements engineers to: (1) understand ML performance measures to state good functional requirements, (2) be aware of new quality requirements such as explainability, freedom from discrimination, or specific legal requirements, and (3) integrate ML specifics in the RE process.
SZZ Unleashed: An Open Implementation of the SZZ Algorithm - Featuring Example Usage in a Study of Just-in-Time Bug Prediction for the Jenkins Project
We present SZZ Unleashed, an open implementation of the SZZ algorithm for git repositories.
Software Engineering
Towards Structured Evaluation of Deep Neural Network Supervisors
Deep Neural Networks (DNN) have improved the quality of several non-safety related products in the past years.
On Using Active Learning and Self-Training when Mining Performance Discussions on Stack Overflow
In a classification task with limited resources, Active Learning (AL) promises to guide annotators to examples that bring the most value for a classifier.
