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As software systems grow in complexity and the space of possibleconfigurations increases exponentially, finding the near-optimal configurationof a software system becomes challenging. Recent approaches address thischallenge by learning performance models based on a sample set ofconfigurations. However, collecting enough sample configurations can be veryexpensive since each such sample requires configuring, compiling, and executingthe entire system using a complex test suite. When learning on new data is tooexpensive, it is possible to use \textit{Transfer Learning} to "transfer" oldlessons to the new context. Traditional transfer learning has a number ofchallenges, specifically, (a) learning from excessive data takes excessivetime, and (b) the performance of the models built via transfer can deteriorateas a result of learning from a poor source. To resolve these problems, wepropose a novel transfer learning framework called BEETLE, which is a"bellwether"-based transfer learner that focuses on identifying and learningfrom the most relevant source from amongst the old data. This paper evaluatesBEETLE with 57 different software configuration problems based on five softwaresystems (a video encoder, an SAT solver, a SQL database, a high-performanceC-compiler, and a streaming data analytics tool). In each of these cases,BEETLE found configurations that are as good as or better than those found byother state-of-the-art transfer learners while requiring only a fraction($\frac{1}{7}$th) of the measurements needed by those other methods. Based onthese results, we say that BEETLE is a new high-water mark in optimallyconfiguring software.

Whence to Learn? Transferring Knowledge in Configurable Systems using BEETLE