TY - GEN
T1 - Distance-Based Sampling of Software Configuration Spaces
AU - Kaltenecker, Christian
AU - Grebhahn, Alexander
AU - Siegmund, Norbert
AU - Guo, Jianmei
AU - Apel, Sven
N1 - Publisher Copyright:
© 2020 Gesellschaft fur Informatik (GI). All rights reserved.
PY - 2020
Y1 - 2020
N2 - Configurable software systems provide configuration options to adjust and optimize their functional and non-functional properties. However, to obtain accurate performance predictions, a representative sample set of configurations is required. Different sampling strategies have been proposed, which come with different advantages and disadvantages. In our experiments, we found that most sampling strategies do not achieve a good coverage of the configuration space with respect to covering relevant performance values. That is, they miss important configurations with distinct performance behavior. Based on this observation, we devise a new sampling strategy that is based on a distance metric and a probability distribution to spread the configurations of the sample set across the configuration space. To demonstrate the merits of distance-based sampling, we compare it to state-of-the-art sampling strategies on 10 real-world configurable software systems. Our results show that distance-based sampling leads to more accurate performance models for medium to large sample sets.
AB - Configurable software systems provide configuration options to adjust and optimize their functional and non-functional properties. However, to obtain accurate performance predictions, a representative sample set of configurations is required. Different sampling strategies have been proposed, which come with different advantages and disadvantages. In our experiments, we found that most sampling strategies do not achieve a good coverage of the configuration space with respect to covering relevant performance values. That is, they miss important configurations with distinct performance behavior. Based on this observation, we devise a new sampling strategy that is based on a distance metric and a probability distribution to spread the configurations of the sample set across the configuration space. To demonstrate the merits of distance-based sampling, we compare it to state-of-the-art sampling strategies on 10 real-world configurable software systems. Our results show that distance-based sampling leads to more accurate performance models for medium to large sample sets.
KW - Configurable Systems
KW - Configuration Sampling
KW - Distance-Based Sampling
KW - Performance Modeling
UR - https://www.scopus.com/pages/publications/85138680922
U2 - 10.18420/SE2020_17
DO - 10.18420/SE2020_17
M3 - 会议稿件
AN - SCOPUS:85138680922
T3 - Lecture Notes in Informatics (LNI), Proceedings - Series of the Gesellschaft fur Informatik (GI)
SP - 61
EP - 64
BT - Software Engineering 2020 - Fachtagung des GI-Fachbereichs Softwaretechnik
A2 - Felderer, Michael
A2 - Hasselbring, Wilhelm
A2 - Rabiser, Rick
A2 - Jung, Reiner
PB - Gesellschaft fur Informatik (GI)
T2 - Fachtagung des GI-Fachbereichs Softwaretechnik, Software Engineering 2020 - Conference of the GI Special Interest Group on Software Engineering, Software Engineering 2020
Y2 - 24 February 2020 through 28 February 2020
ER -