2017 11th Joint Meeting of the European Software Engineering Conference and the ACM SIGSOFT Symposium on the Foundations of Software Engineering (ESEC/FSE 2017), September 4–8, 2017, Paderborn, Germany

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Machine Learning
Research Papers
S3, Chairs: Arosha Bandara
Finding Near-Optimal Configurations in Product Lines by Random Sampling
Jeho Oh, Don Batory, Margaret Myers, and Norbert Siegmund
(University of Texas at Austin, USA; Bauhaus-University Weimar, Germany)
Publisher's Version
Abstract: Software Product Lines (SPLs) are highly configurable systems. This raises the challenge to find optimal performing configurations for an anticipated workload. As SPL configuration spaces are huge, it is infeasible to benchmark all configurations to find an optimal one. Prior work focused on building performance models to predict and optimize SPL configurations. Instead, we randomly sample and recursively search a configuration space directly to find near-optimal configurations without constructing a prediction model. Our algorithms are simpler and have higher accuracy and efficiency.


Time stamp: 2020-02-22T23:40:08+01:00