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T. Chen and M. Li. Multi-objectivizing software configuration tuning. The 29th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE), 453–465, 2021. [PDF] [Java code]
T. Chen and M. Li. Multi-objectivizing software configuration tuning. The 29th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE), 453–465, 2021. [PDF] [Java code]
What do we know about the problem
What do we know about the problem
Black-box;
Expensive (i.e. taking a few minute to perform one evaluation);
Rugged and sparse landscape with numerous local optima;
There exist other performance attributes (e.g. throughput in addition to latency).
Landscape of the objective latency with respect to Splitters and Counters (out of the six configuration options)
Idea
Idea
Given the characteristics of the problem, we use an additional performance attribute to construct an artificial bi-objective optimisation model to help the search jump out of local optima.
Result
Result
Experiments show the effectiveness of our model - up to 42% gain, while using as low as 24% computational resources than state of the arts.
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