Testing machine learning based systems: a systematic map**

V Riccio, G Jahangirova, A Stocco… - Empirical Software …, 2020 - Springer
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 …

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 …

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 …

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 …

Mind the gap! A study on the transferability of virtual versus physical-world testing of autonomous driving systems

A Stocco, B Pulfer, P Tonella - IEEE Transactions on Software …, 2022 - ieeexplore.ieee.org
Safe deployment of self-driving cars (SDC) necessitates thorough simulated and in-field
testing. Most testing techniques consider virtualized SDCs within a simulation environment …

Software verification and validation of safe autonomous cars: A systematic literature review

N Rajabli, F Flammini, R Nardone, V Vittorini - IEEE Access, 2020 - ieeexplore.ieee.org
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 …

A survey on automated driving system testing: Landscapes and trends

S Tang, Z Zhang, Y Zhang, J Zhou, Y Guo… - ACM Transactions on …, 2023 - dl.acm.org
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 …

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 …

Simple techniques work surprisingly well for neural network test prioritization and active learning (replicability study)

M Weiss, P Tonella - Proceedings of the 31st ACM SIGSOFT …, 2022 - dl.acm.org
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 …

Thirdeye: Attention maps for safe autonomous driving systems

A Stocco, PJ Nunes, M d'Amorim… - Proceedings of the 37th …, 2022 - dl.acm.org
Automated online recognition of unexpected conditions is an indispensable component of
autonomous vehicles to ensure safety even in unknown and uncertain situations. In this …