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Transferring Performance between Distinct Configurable Systems : A Case Study

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Abstract

Many research studies predict the performance of configurable software using machine learning techniques, thus requiring large amounts of data. Transfer learning aims to reduce the amount of data needed to train these models and has been successfully applied on different executing environments (hardware) or software versions. In this paper we investigate for the first time the idea of applying transfer learning between distinct configurable systems. We design a study involving two video encoders (namely x264 and x265) coming from different code bases. Our results are encouraging since transfer learning outperforms traditional learning for two performance properties (out of three). We discuss the open challenges to overcome for a more general application.
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Dates and versions

hal-03514984 , version 1 (06-01-2022)

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Luc Lesoil, Hugo Martin, Mathieu Acher, Arnaud Blouin, Jean-Marc Jézéquel. Transferring Performance between Distinct Configurable Systems : A Case Study. VaMoS 2022 - 16th International Working Conference on Variability Modelling of Software-Intensive Systems, Feb 2022, Florence, Italy. pp.1-6, ⟨10.1145/3510466.3510486⟩. ⟨hal-03514984⟩
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