Performance prediction of configurable software systems by fourier learning

Yi Zhang, Jianmei Guo, Eric Blais, Krzysztof Czarnecki

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

73 Scopus citations

Abstract

Understanding how performance varies across a large number of variants of a configurable software system is important for helping stakeholders to choose a desirable variant. Given a software system with n optional features, measuring all its 2n possible configurations to determine their performances is usually infeasible. Thus, various techniques have been proposed to predict software performances based on a small sample of measured configurations. We propose a novel algorithm based on Fourier transform that is able to make predictions of any configurable software system with theoretical guarantees of accuracy and confidence level specified by the user, while using minimum number of samples up to a constant factor. Empirical results on the case studies constructed from real-world configurable systems demonstrate the effectiveness of our algorithm.

Original languageEnglish
Title of host publicationProceedings - 2015 30th IEEE/ACM International Conference on Automated Software Engineering, ASE 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages365-373
Number of pages9
ISBN (Electronic)9781509000241
DOIs
StatePublished - 4 Jan 2016
Externally publishedYes
Event30th IEEE/ACM International Conference on Automated Software Engineering, ASE 2015 - Lincoln, United States
Duration: 9 Nov 201513 Nov 2015

Publication series

NameProceedings - 2015 30th IEEE/ACM International Conference on Automated Software Engineering, ASE 2015

Conference

Conference30th IEEE/ACM International Conference on Automated Software Engineering, ASE 2015
Country/TerritoryUnited States
CityLincoln
Period9/11/1513/11/15

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