61 citations · 319 across the 56 of their papers we have counts for
10 papers · 1 filter
Developing Self-Adaptive Microservice Systems: Challenges and Directions
Nabor C. Mendonca, Pooyan Jamshidi, David Garlan +1
A self-adaptive system can dynamically monitor and adapt its behavior to preserve or enhance its quality attributes under uncertain operating conditions. This article identifies ke…
Whence to Learn? Transferring Knowledge in Configurable Systems using BEETLE
Rahul Krishna, Vivek Nair, Pooyan Jamshidi +1
As software systems grow in complexity and the space of possible configurations increases exponentially, finding the near-optimal configuration of a software system becomes challen…
How Do Code Changes Evolve in Different Platforms? A Mining-based Investigation
Markos Viggiato, Johnatan Oliveira, Eduardo Figueiredo +2
Code changes are performed differently in the mobile and non-mobile platforms. Prior work has investigated the differences in specific platforms. However, we still lack a deeper un…
Kuksa: A Cloud-Native Architecture for Enabling Continuous Delivery in the Automotive Domain
Ahmad Banijamali, Pooyan Jamshidi, Pasi Kuvaja +1
Connecting vehicles to cloud platforms has enabled innovative business scenarios while raising new quality concerns, such as reliability and scalability, which must be addressed by…
Order-Independent Structure Learning of Multivariate Regression Chain Graphs
Mohammad Ali Javidian, Marco Valtorta, Pooyan Jamshidi
This paper deals with multivariate regression chain graphs (MVR CGs), which were introduced by Cox and Wermuth [3,4] to represent linear causal models with correlated errors. We co…
Conservative Q-Improvement: Reinforcement Learning for an Interpretable Decision-Tree Policy
Aaron M. Roth, Nicholay Topin, Pooyan Jamshidi +1
There is a growing desire in the field of reinforcement learning (and machine learning in general) to move from black-box models toward more "interpretable AI." We improve interpre…
ConfigCrusher: Towards White-Box Performance Analysis for Configurable Systems
Miguel Velez, Pooyan Jamshidi, Florian Sattler +3
Stakeholders of configurable systems are often interested in knowing how configuration options influence the performance of a system to facilitate, for example, the debugging and o…
Transfer Learning for Performance Modeling of Deep Neural Network Systems
Md Shahriar Iqbal, Lars Kotthoff, Pooyan Jamshidi
Modern deep neural network (DNN) systems are highly configurable with large a number of options that significantly affect their non-functional behavior, for example inference time…
Machine Learning Meets Quantitative Planning: Enabling Self-Adaptation in Autonomous Robots
Pooyan Jamshidi, Javier Cámara, Bradley Schmerl +2
Modern cyber-physical systems (e.g., robotics systems) are typically composed of physical and software components, the characteristics of which are likely to change over time. Assu…
Transfer Learning for Performance Modeling of Configurable Systems: A Causal Analysis
Mohammad Ali Javidian, Pooyan Jamshidi, Marco Valtorta
Modern systems (e.g., deep neural networks, big data analytics, and compilers) are highly configurable, which means they expose different performance behavior under different confi…