279 citations · 1.1k across the 57 of their papers we have counts for
68 papers
Naming the Pain in Machine Learning-Enabled Systems Engineering
Marcos Kalinowski, Daniel Mendez, Görkem Giray +12
Context: Machine learning (ML)-enabled systems are being increasingly adopted by companies aiming to enhance their products and operational processes. Objective: This paper aims to…
ML-Enabled Systems Model Deployment and Monitoring: Status Quo and Problems
Eduardo Zimelewicz, Marcos Kalinowski, Daniel Mendez +13
[Context] Systems incorporating Machine Learning (ML) models, often called ML-enabled systems, have become commonplace. However, empirical evidence on how ML-enabled systems are en…
Status Quo and Problems of Requirements Engineering for Machine Learning: Results from an International Survey
Antonio Pedro Santos Alves, Marcos Kalinowski, Görkem Giray +12
Systems that use Machine Learning (ML) have become commonplace for companies that want to improve their products and processes. Literature suggests that Requirements Engineering (R…
Towards Model Co-evolution Across Self-Adaptation Steps for Combined Safety and Security Analysis
Thomas Witte, Raffaela Groner, Alexander Raschke +3
Self-adaptive systems offer several attack surfaces due to the communication via different channels and the different sensors required to observe the environment. Often, attacks ca…
VULNERLIZER: Cross-analysis Between Vulnerabilities and Software Libraries
Irdin Pekaric, Michael Felderer, Philipp Steinmüller
The identification of vulnerabilities is a continuous challenge in software projects. This is due to the evolution of methods that attackers employ as well as the constant updates…
Simulation of Sensor Spoofing Attacks on Unmanned Aerial Vehicles Using the Gazebo Simulator
Irdin Pekaric, David Arnold, Michael Felderer
Conducting safety simulations in various simulators, such as the Gazebo simulator, became a very popular means of testing vehicles against potential safety risks (i.e. crashes). Ho…