arXiv:2611.16566v1 Announce Type: cross Abstract: Steady-state detection is a central step in software performance benchmarking, yet different detection methods rely on different assumptions and may perform best on different execution profiles. This paper formulates steady-state detection as an algorithm-recommendation problem: given a benchmark time series, predict which of two complementary detectors is more appropriate. The first, CP-SSD, identifies steady state through changepoint-based segmentation of the execution trace, whereas the second, KB-KSSD, uses a kernel-based approach to identify stable behaviour. We propose a data-driven recommender that characterises benchmark time series using automatically extracted features and combines supervised prediction with information derived from groups of time series exhibiting similar behaviour. The approach is evaluated on 407 labelled benchmark time series for which one of the two detectors can be identified as preferable with respect to a reference steady-state index. The proposed recommender selects the better detector correctly for 72.97% of the series, compared with 62.65% when always selecting the detector that performs best most often, corresponding to an absolute improvement of 10.32 percentage points. Paired statistical testing and bootstrap confidence intervals support this improvement. A comparison with a recommender using time-series features alone shows that the cluster-derived information mainly improves interpretability rather than predictive accuracy. Recommendations also remain highly stable when the unsupervised representation is learned from either the labelled subset or the full collection of 5,860 time series. These results demonstrate the feasibility of per-series steady-state detector recommendation while highlighting the need for broader labelled data, particularly for execution profiles favouring the changepoint-based detector.
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