123 citations · 154 across the 12 of their papers we have counts for
13 papers · 1 filter
Socio-Technical Anti-Patterns in Building ML-Enabled Software: Insights from Leaders on the Forefront
Alina Mailach, Norbert Siegmund
Although machine learning (ML)-enabled software systems seem to be a success story considering their rise in economic power, there are consistent reports from companies and practit…
Detecting Performance-Relevant Changes in Configurable Software Systems
Sebastian Böhm, Florian Sattler, Norbert Siegmund +1
Performance is a volatile property of a software system and frequent performance profiling is required to keep the knowledge about a software system's performance behavior up to da…
On Debugging the Performance of Configurable Software Systems: Developer Needs and Tailored Tool Support
Miguel Velez, Pooyan Jamshidi, Norbert Siegmund +2
Determining whether a configurable software system has a performance bug or it was misconfigured is often challenging. While there are numerous debugging techniques that can suppor…
VEER: Enhancing the Interpretability of Model-based Optimizations
Kewen Peng, Christian Kaltenecker, Norbert Siegmund +2
Many software systems can be tuned for multiple objectives (e.g., faster runtime, less required memory, less network traffic or energy consumption, etc.). Optimizers built for diff…
White-Box Performance-Influence Models: A Profiling and Learning Approach
Max Weber, Sven Apel, Norbert Siegmund
Many modern software systems are highly configurable, allowing the user to tune them for performance and more. Current performance modeling approaches aim at finding performance-op…
White-Box Analysis over Machine Learning: Modeling Performance of Configurable Systems
Miguel Velez, Pooyan Jamshidi, Norbert Siegmund +2
Performance-influence models can help stakeholders understand how and where configuration options and their interactions influence the performance of a system. With this understand…
Predicting Performance of Software Configurations: There is no Silver Bullet
Alexander Grebhahn, Norbert Siegmund, Sven Apel
Many software systems offer configuration options to tailor their functionality and non-functional properties (e.g., performance). Often, users are interested in the (performance-)…
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…
On the Relation of External and Internal Feature Interactions: A Case Study
Sergiy Kolesnikov, Norbert Siegmund, Christian Kästner +1
Detecting feature interactions is imperative for accurately predicting performance of highly-configurable systems. State-of-the-art performance prediction techniques rely on superv…
Finding Faster Configurations using FLASH
Vivek Nair, Zhe Yu, Tim Menzies +2
Finding good configurations for a software system is often challenging since the number of configuration options can be large. Software engineers often make poor choices about conf…
Transfer Learning for Improving Model Predictions in Highly Configurable Software
Pooyan Jamshidi, Miguel Velez, Christian Kästner +2
Modern software systems are built to be used in dynamic environments using configuration capabilities to adapt to changes and external uncertainties. In a self-adaptation context,…
Using Bad Learners to find Good Configurations
Vivek Nair, Tim Menzies, Norbert Siegmund +1
Finding the optimally performing configuration of a software system for a given setting is often challenging. Recent approaches address this challenge by learning performance model…
Faster Discovery of Faster System Configurations with Spectral Learning
Vivek Nair, Tim Menzies, Norbert Siegmund +1
Despite the huge spread and economical importance of configurable software systems, there is unsatisfactory support in utilizing the full potential of these systems with respect to…