Search Results for author: Sven Apel

Found 6 papers, 2 papers with code

Feature Interactions on Steroids: On the Composition of ML Models

no code implementations13 May 2021 Christian Kästner, Eunsuk Kang, Sven Apel

The lack of specifications is a key difference between traditional software engineering and machine learning.

A Decision Tree Lifted Domain for Analyzing Program Families with Numerical Features (Extended Version)

no code implementations10 Dec 2020 Aleksandar S. Dimovski, Sven Apel, Axel Legay

Lifted (family-based) static analysis by abstract interpretation is capable of analyzing all variants of a program family simultaneously, in a single run without generating any of the variants explicitly.

Programming Languages Logic in Computer Science Software Engineering

Predicting Performance of Software Configurations: There is no Silver Bullet

no code implementations28 Nov 2019 Alexander Grebhahn, Norbert Siegmund, Sven Apel

We have conducted a comparative study regarding the mean prediction accuracy when predicting the performance of all configurations considering 6 machine-learning techniques, 18 sampling strategies, and 6 subject software systems.

Finding Faster Configurations using FLASH

1 code implementation7 Jan 2018 Vivek Nair, Zhe Yu, Tim Menzies, Norbert Siegmund, Sven Apel

FLASH scales up to software systems that defeat the prior state of the art model-based methods in this area.

Software Engineering

Effectiveness of Anonymization in Double-Blind Review

1 code implementation5 Sep 2017 Claire Le Goues, Yuriy Brun, Sven Apel, Emery Berger, Sarfraz Khurshid, Yannis Smaragdakis

Double-blind review relies on the authors' ability and willingness to effectively anonymize their submissions.

Digital Libraries General Literature Software Engineering

Faster Discovery of Faster System Configurations with Spectral Learning

no code implementations27 Jan 2017 Vivek Nair, Tim Menzies, Norbert Siegmund, Sven Apel

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 finding performance-optimal configurations.

Dimensionality Reduction

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