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08/29/2023
Introducing Interactions in Multi-Objective Optimization of Software Architectures
Software architecture optimization aims to enhance non-functional attrib...
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08/24/2023
IPA: Inference Pipeline Adaptation to Achieve High Accuracy and Cost-Efficiency
Efficiently optimizing multi-model inference pipelines for fast, accurat...
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06/13/2023
CAMEO: A Causal Transfer Learning Approach for Performance Optimization of Configurable Computer Systems
Modern computer systems are highly-configurable, with hundreds of config...
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06/02/2023
Independent Modular Networks
Monolithic neural networks that make use of a single set of weights to l...
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04/21/2023
Reconciling High Accuracy, Cost-Efficiency, and Low Latency of Inference Serving Systems
The use of machine learning (ML) inference for various applications is g...
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02/05/2023
On the Role of Contrastive Representation Learning in Adversarial Robustness: An Empirical Study
Self-supervised contrastive learning has solved one of the significant o...
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01/18/2023
CaRE: Finding Root Causes of Configuration Issues in Highly-Configurable Robots
Robotic systems have several subsystems that possess a huge combinatoria...
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09/18/2022
Improving the Performance of DNN-based Software Services using Automated Layer Caching
Deep Neural Networks (DNNs) have become an essential component in many a...
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06/24/2022
Guidelines for Artifacts to Support Industry-Relevant Research on Self-Adaptation
Artifacts support evaluating new research results and help comparing the...
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03/19/2022
On Debugging the Performance of Configurable Software Systems: Developer Needs and Tailored Tool Support
Determining whether a configurable software system has a performance bug...
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01/25/2022
Pre-Trained Language Transformers are Universal Image Classifiers
Facial images disclose many hidden personal traits such as age, gender, ...
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01/20/2022
Unicorn: Reasoning about Configurable System Performance through the lens of Causality
Modern computer systems are highly configurable, with the total variabil...
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10/07/2021
Pretrained Language Models are Symbolic Mathematics Solvers too!
Solving symbolic mathematics has always been of in the arena of human in...
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02/27/2021
Scalable Causal Transfer Learning
One of the most important problems in transfer learning is the task of d...
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02/23/2021
Accelerating Recursive Partition-Based Causal Structure Learning
Causal structure discovery from observational data is fundamental to the...
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01/13/2021
White-Box Analysis over Machine Learning: Modeling Performance of Configurable Systems
Performance-influence models can help stakeholders understand how and wh...
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01/10/2021
Kuksa*: Self-Adaptive Microservices in Automotive Systems
In pervasive dynamic environments, vehicles connect to other objects to ...
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10/12/2020
CADET: A Systematic Method For Debugging Misconfigurations using Counterfactual Reasoning
Modern computing platforms are highly-configurable with thousands of int...
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05/29/2020
Learning LWF Chain Graphs: A Markov Blanket Discovery Approach
This paper provides a graphical characterization of Markov blankets in c...
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05/27/2020
Learning LWF Chain Graphs: an Order Independent Algorithm
LWF chain graphs combine directed acyclic graphs and undirected graphs. ...
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05/13/2020
Understanding the Nature of System-Related Issues in Machine Learning Frameworks: An Exploratory Study
Modern systems are built using development frameworks. These frameworks ...
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04/17/2020
A Procedure for Extracting Software Development Process Patterns
Process patterns represent well-structured and successful recurring acti...
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04/17/2020
Process Patterns for Service Oriented Development
Software systems development nowadays has moved towards dynamic composit...
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04/17/2020
Toward a Methodological Knowledge for Service-Oriented Development Based on OPEN Meta Model
Situational method engineering uses a repository of reusable method frag...
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04/17/2020
Enhancing the OPEN Process Framework with Service-Oriented Method Fragments
Service-orientation is a promising paradigm that enables the engineering...
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02/24/2020
AMP Chain Graphs: Minimal Separators and Structure Learning Algorithms
We address the problem of finding a minimal separator in an Andersson-Ma...
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01/18/2020
FlexiBO: Cost-Aware Multi-Objective Optimization of Deep Neural Networks
One of the key challenges in designing machine learning systems is to de...
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01/02/2020
Ensembles of Many Diverse Weak Defenses can be Strong: Defending Deep Neural Networks Against Adversarial Attacks
Despite achieving state-of-the-art performance across many domains, mach...
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11/01/2019
Whence to Learn? Transferring Knowledge in Configurable Systems using BEETLE
As software systems grow in complexity and the space of possible configu...
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10/24/2019
How Do Code Changes Evolve in Different Platforms? A Mining-based Investigation
Code changes are performed differently in the mobile and non-mobile plat...
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10/22/2019
Kuksa: A Cloud-Native Architecture for Enabling Continuous Delivery in the Automotive Domain
Connecting vehicles to cloud platforms has enabled innovative business s...
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10/17/2019
Developing Self-Adaptive Microservice Systems: Challenges and Directions
A self-adaptive system can dynamically monitor and adapt its behavior to...
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10/01/2019
Order-Independent Structure Learning of Multivariate Regression Chain Graphs
This paper deals with multivariate regression chain graphs (MVR CGs), wh...
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07/02/2019
Conservative Q-Improvement: Reinforcement Learning for an Interpretable Decision-Tree Policy
There is a growing desire in the field of reinforcement learning (and ma...
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05/06/2019
ConfigCrusher: White-Box Performance Analysis for Configurable Systems
In configurable software systems, stakeholders are often interested in k...
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04/04/2019
Transfer Learning for Performance Modeling of Deep Neural Network Systems
Modern deep neural network (DNN) systems are highly configurable with la...
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03/10/2019
Machine Learning Meets Quantitative Planning: Enabling Self-Adaptation in Autonomous Robots
Modern cyber-physical systems (e.g., robotics systems) are typically com...
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02/26/2019
Transfer Learning for Performance Modeling of Configurable Systems: A Causal Analysis
Modern systems (e.g., deep neural networks, big data analytics, and comp...
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08/21/2018
How is Performance Addressed in DevOps? A Survey on Industrial Practices
DevOps is a modern software engineering paradigm that is gaining widespr...
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03/11/2018
Transfer Learning with Bellwethers to find Good Configurations
As software systems grow in complexity, the space of possible configurat...
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01/15/2018
An Efficient Method for Uncertainty Propagation in Robust Software Performance Estimation
Software engineers often have to estimate the performance of a software ...
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09/26/2017
Report from GI-Dagstuhl Seminar 16394: Software Performance Engineering in the DevOps World
This report documents the program and the outcomes of GI-Dagstuhl Semina...
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09/07/2017
Transfer Learning for Performance Modeling of Configurable Systems: An Exploratory Analysis
Modern software systems provide many configuration options which signifi...
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05/19/2017