87 citations · 135 across the 5 of their papers we have counts for
7 papers
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…
Towards Better Adaptive Systems by Combining MAPE, Control Theory, and Machine Learning
Danny Weyns, Bradley Schmerl, Masako Kishida +5
Two established approaches to engineer adaptive systems are architecture-based adaptation that uses a Monitor-Analysis-Planning-Executing (MAPE) loop that reasons over architectura…
DeepCert: Verification of Contextually Relevant Robustness for Neural Network Image Classifiers
Colin Paterson, Haoze Wu, John Grese +3
We introduce DeepCert, a tool-supported method for verifying the robustness of deep neural network (DNN) image classifiers to contextually relevant perturbations such as blur, haze…
Guidance on the Assurance of Machine Learning in Autonomous Systems (AMLAS)
Richard Hawkins, Colin Paterson, Chiara Picardi +3
Machine Learning (ML) is now used in a range of systems with results that are reported to exceed, under certain conditions, human performance. Many of these systems, in domains suc…
Assuring the Machine Learning Lifecycle: Desiderata, Methods, and Challenges
Rob Ashmore, Radu Calinescu, Colin Paterson
Machine learning has evolved into an enabling technology for a wide range of highly successful applications. The potential for this success to continue and accelerate has placed ma…
Efficient Parametric Model Checking Using Domain Knowledge
Radu Calinescu, Colin Paterson, Kenneth Johnson
We introduce an efficient parametric model checking (ePMC) method for the analysis of reliability, performance and other quality-of-service (QoS) properties of software systems. eP…