activity
20172026
most citedAssuring the Machine Learning Lifecycle: Desiderata, Methods, and Challenges

87 citations · 177 across the 30 of their papers we have counts for

collaborators
Showing 2022Show all

6 papers · 1 filter

cs.SE2022

Specification Architectural Viewpoint for Benefit-Cost-Risk-Aware Decision-Making in Self-Adaptive Systems

Danny Weyns, Paris Avegriou, Radu Calinescu +3

Over the past two decades, researchers and engineers have extensively studied the problem of how to enable a software system to deal with uncertain operating conditions. One promin…

cs.RO2022★ 5 cited

Towards Adaptive Planning of Assistive-care Robot Tasks

Jordan Hamilton, Ioannis Stefanakos, Radu Calinescu +1

This 'research preview' paper introduces an adaptive path planning framework for robotic mission execution in assistive-care applications. The framework provides a graph-based envi…

cs.LO2022★ 6 cited

Scheduling of Missions with Constrained Tasks for Heterogeneous Robot Systems

Gricel Vázquez, Radu Calinescu, Javier Cámara

We present a formal tasK AllocatioN and scheduling apprOAch for multi-robot missions (KANOA). KANOA supports two important types of task constraints: task ordering, which requires…

cs.LO2022

Software Performability Analysis Using Fast Parametric Model Checking

Xinwei Fang, Radu Calinescu, Simos Gerasimou +1

We present an efficient parametric model checking (PMC) technique for the analysis of software performability, i.e., of the performance and dependability properties of software sys…

cs.SE2022

PRESTO: Predicting System-level Disruptions through Parametric Model Checking

Xinwei Fang, Radu Calinescu, Colin Paterson +1

Self-adaptive systems are expected to mitigate disruptions by continually adjusting their configuration and behaviour. This mitigation is often reactive. Typically, environmental o…

cs.LG2022★ 3 cited

Discrete-Event Controller Synthesis for Autonomous Systems with Deep-Learning Perception Components

Radu Calinescu, Calum Imrie, Ravi Mangal +4

We present DeepDECS, a new method for the synthesis of correct-by-construction discrete-event controllers for autonomous systems that use deep neural network (DNN) classifiers for…