Martin Beseda, Vittorio Cortellessa, Federico Di Menna, Daniele Di Pompeo, Luca Traini
原文摘要
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.
Regression testing assesses whether software components that have already been developed and tested continue to work correctly after modifications. Ideally, regression testing re-executes the entire test suite after each modification, but this is infeasible in real-world scenarios because of the computational burden; therefore, optimized approaches, such as Test Case Selection, have been developed. The state of the art in regression testing optimization includes both classical approaches, such as Additional Greedy and DIV-GA, and quantum approaches, such as SelectQA, BootQA, and IGDec-QAOA. However, while the former incur high computational costs, the latter are constrained by the physical limitations of currently available quantum machines. This paper extends the evaluation of QAOA-TCS, our quantum approach for the test case selection problem. We thoroughly evaluate its performance, compare it with state-of-the-art classical and quantum approaches, and assess its robustness to quantum noise. The results show that QAOA-TCS outperforms state-of-the-art multi-objective approaches in most cases and maintains stable performance even under high noise levels. In the single-objective formulation, QAOA-based methods perform comparably. In terms of efficiency, our previous method based on quantum annealing, namely SelectQA, is the most efficient for large problems in the multi-objective formulation, whereas QAOA-TCS offers the best trade-off between effectiveness and efficiency in the single-objective formulation when run on realistic noisy simulators. However, such analysis is constrained by the different computational environments used to run the algorithms.