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Surrogate-assisted search-based testing (SA-SBT) aims to reduce thecomputational time for testing compute-intensive systems. Surrogates enhancetesting techniques by improving test case generation focusing the testingbudget on the most critical portions of the input domain. In addition, they canserve as approximations of the system under test (SUT) to predict tests'results instead of executing the tests on compute-intensive SUTs. This articlereflects on the existing SA-SBT techniques, particularly those applied tosystem-level testing and often facilitated using simulators or complex testbeds. Our objective is to synthesize different heuristic algorithms andevaluation methods employed in existing SA-SBT techniques and present acomprehensive view of SA-SBT solutions. In addition, by critically reviewingour previous work on SA-SBT, we aim to identify the limitations in our proposedalgorithms and evaluation methods and to propose potential improvements. Wepresent a taxonomy that categorizes and contrasts existing SA-SBT solutions andhighlights key research gaps. To identify the evaluation challenges, we conducttwo replication studies of our past SA-SBT solutions: One study uses industrialadvanced driver assistance system (ADAS) and the other relies on a Simulinkmodel benchmark. We compare our results with those of the original studies andidentify the difficulties in evaluating SA-SBT techniques, including the impactof different contextual factors on results generalization and the validity ofour evaluation metrics. Based on our taxonomy and replication studies, wepropose future research directions, including re-considerations in the currentevaluation metrics used for SA-SBT solutions, utilizing surrogates for faultlocalization and repair in addition to testing, and creating frameworks forlarge-scale experiments by applying SA-SBT to multiple SUTs and simulators.

Reflections on Surrogate-Assisted Search-Based Testing: A Taxonomy and Two Replication Studies based on Industrial ADAS and Simulink Models