24 citations · 35 across the 10 of their papers we have counts for
12 papers · 1 filter
Learning Domain-Specific Edit Operations from Model Repositories with Frequent Subgraph Mining
Christof Tinnes, Timo Kehrer, Mitchell Joblin +3
Model transformations play a fundamental role in model-driven software development. They can be used to solve or support central tasks, such as creating models, handling model co-e…
In Search of Socio-Technical Congruence: A Large-Scale Longitudinal Study
Wolfgang Mauerer, Mitchell Joblin, Damian A. Tamburri +3
We report on a large-scale empirical study investigating the relevance of socio-technical congruence over key basic software quality metrics, namely, bugs and churn. In particular,…
Feature Interactions on Steroids: On the Composition of ML Models
Christian Kästner, Eunsuk Kang, Sven Apel
The lack of specifications is a key difference between traditional software engineering and machine learning. We discuss how it drastically impacts how we think about divide-and-co…
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-)…