Testing machine learning based systems: a systematic map**
Abstract Context: A Machine Learning based System (MLS) is a software system including
one or more components that learn how to perform a task from a given data set. The …
one or more components that learn how to perform a task from a given data set. The …
A software engineering perspective on engineering machine learning systems: State of the art and challenges
G Giray - Journal of Systems and Software, 2021 - Elsevier
Context: Advancements in machine learning (ML) lead to a shift from the traditional view of
software development, where algorithms are hard-coded by humans, to ML systems …
software development, where algorithms are hard-coded by humans, to ML systems …
Anomaly detection in autonomous driving: A survey
D Bogdoll, M Nitsche… - Proceedings of the IEEE …, 2022 - openaccess.thecvf.com
Nowadays, there are outstanding strides towards a future with autonomous vehicles on our
roads. While the perception of autonomous vehicles performs well under closed-set …
roads. While the perception of autonomous vehicles performs well under closed-set …
A collaborative V2X data correction method for road safety
L Zhao, H Chai, Y Han, K Yu… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Driving safety is one of the most important points to concern on the road. Vehicles constantly
generate messages under vehicle-to-everything (V2X) assisted driving. Especially, in dense …
generate messages under vehicle-to-everything (V2X) assisted driving. Especially, in dense …
Mind the gap! A study on the transferability of virtual versus physical-world testing of autonomous driving systems
Safe deployment of self-driving cars (SDC) necessitates thorough simulated and in-field
testing. Most testing techniques consider virtualized SDCs within a simulation environment …
testing. Most testing techniques consider virtualized SDCs within a simulation environment …
Software verification and validation of safe autonomous cars: A systematic literature review
Autonomous, or self-driving, cars are emerging as the solution to several problems primarily
caused by humans on roads, such as accidents and traffic congestion. However, those …
caused by humans on roads, such as accidents and traffic congestion. However, those …
A survey on automated driving system testing: Landscapes and trends
Automated Driving Systems (ADS) have made great achievements in recent years thanks to
the efforts from both academia and industry. A typical ADS is composed of multiple modules …
the efforts from both academia and industry. A typical ADS is composed of multiple modules …
Big data systems: A software engineering perspective
A Davoudian, M Liu - ACM Computing Surveys (CSUR), 2020 - dl.acm.org
Big Data Systems (BDSs) are an emerging class of scalable software technologies whereby
massive amounts of heterogeneous data are gathered from multiple sources, managed …
massive amounts of heterogeneous data are gathered from multiple sources, managed …
Simple techniques work surprisingly well for neural network test prioritization and active learning (replicability study)
Test Input Prioritizers (TIP) for Deep Neural Networks (DNN) are an important technique to
handle the typically very large test datasets efficiently, saving computation and labelling …
handle the typically very large test datasets efficiently, saving computation and labelling …
Thirdeye: Attention maps for safe autonomous driving systems
Automated online recognition of unexpected conditions is an indispensable component of
autonomous vehicles to ensure safety even in unknown and uncertain situations. In this …
autonomous vehicles to ensure safety even in unknown and uncertain situations. In this …