Blogs (1) >>
ICSE 2019
Sat 25 - Fri 31 May 2019 Montreal, QC, Canada
Fri 31 May 2019 16:00 - 16:20 at Mansfield / Sherbrooke - Configuration and Optimization Chair(s): Caroline Lemieux

Configurable software systems often provide a multitude of configuration options to adjust and optimize their functional and non-functional properties. For instance, to find the fastest configuration for a given setting, a brute-force approach is to measure the performance of all configurations, which is typically intractable. Addressing this challenge, state-of-the-art approaches rely on machine learning, analyzing a few configurations (i.e., a sample set) to predict the performance of other configurations. However, to obtain accurate performance predictions, a representative sample set of configurations is desirable. Addressing this task, different sampling strategies have been proposed, which come with different advantages (e.g., covering the configuration space systematically) and disadvantages (e.g., the need to enumerate all configurations). In our experiments, we found that most sampling strategies 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, called distance-based sampling, that is based on a distance metric and a probability distribution to spread the configurations of the resulting sample set according the probability distribution across the configuration space. This way, we cover different kinds of interactions among configuration options in the sample set. To demonstrate the benefit of distance-based sampling, we compare it to state-of-the-art sampling strategies, such as t-wise sampling, on 10 real-world software systems. Our results show that distance-based sampling leads to higher accuracy of performance models of configurable software systems for medium to large sample-set sizes.

Conference Day
Fri 31 May

Displayed time zone: Eastern Time (US & Canada) change

16:00 - 17:20
Configuration and OptimizationTechnical Track / Journal-First Papers / Papers at Mansfield / Sherbrooke
Chair(s): Caroline LemieuxUniversity of California, Berkeley
16:00
20m
Talk
Distance-Based Sampling of Software Configuration SpacesArtifacts AvailableArtifacts Evaluated ReusableTechnical Track
Technical Track
Christian KalteneckerSaarland University, Germany, Alexander GrebhahnUniversity of Passau, Norbert SiegmundBauhaus-University Weimar, Jianmei GuoAlibaba Group, Sven ApelSaarland University
Pre-print
16:20
20m
Talk
DeepPerf: Performance Prediction for Configurable Software with Deep Sparse Neural NetworkArtifacts AvailableTechnical Track
Technical Track
Huong HaUniversity of Newcastle, Hongyu ZhangThe University of Newcastle
16:40
10m
Talk
Software Configuration Engineering in Practice - Interviews, Survey, and Systematic Literature ReviewIndustry ProgramJournal-First
Journal-First Papers
Mohammed SayaghMCIS, École Polytechnique de Montréal, Noureddine KerzaziEnsias-Rabat, Bram AdamsMCIS, École Polytechnique de Montréal, Fabio PetrilloUniversité du Québec à Chicoutimi, Canada
16:50
10m
Talk
Finding Faster Configurations using FLASHIndustry ProgramJournal-First
Journal-First Papers
Vivek Nair, Zhe Yu, Tim MenziesNorth Carolina State University, Norbert SiegmundBauhaus-University Weimar, Sven ApelSaarland University
Pre-print
17:00
10m
Talk
FEMOSAA: Feature-Guided and Knee-Driven Multi-Objective Optimization for Self-Adaptive SoftwareJournal-First
Journal-First Papers
Tao ChenNottingham Trent University, UK and University of Birmingham, UK, Ke LiUniversity of Electronic Science and Technology of China; University of Exeter, Rami BahsoonUniversity of Birmingham, Xin Yao
17:10
10m
Talk
Discussion Period
Papers