activity
20172022
most citedPredicting Performance of Software Configurations: There is no Silver Bullet

6 citations · 10 across the 6 of their papers we have counts for

collaborators

7 papers

cs.SE20221 cited

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…

cs.SE20211 cited

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…

cs.SE2021

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…

cs.SE20196 cited

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-)…

cs.SE2019

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…

cs.SE20182 cited

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…