Applied Sciences· 2026Q2
Surrogate-Assisted Search-Based Testing of Robot Controllers Based on Enzymatic Numerical P Systems
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- Q2SCImago
- 2026year
Short summary
A novel gray-box surrogate model, reconstructed from a robot controller's internal rules, effectively predicts safety-critical failures in reactive closed-loop environments, outperforming traditional black-box surrogates.
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Key points
- Developed and evaluated two surrogate strategies for SA-SBT of ENPS-based robot controllers.
- A black-box classifier using road geometry and ENPS model identifier reduced simulations while retaining nearly all true failures.
- A novel gray-box surrogate, reconstructed from internal controller rules, was evaluated for continuous fitness prediction in reactive, closed-loop environments.
AI-generated from the title and abstract; the full text is not read.
Abstract
Surrogate-assisted search-based testing (SA-SBT) reduces simulation cost by replacing expensive runs with lightweight mathematical or data-driven approximations to efficiently guide metaheuristic search toward safety-critical system failures. While existing SA-SBT techniques predominantly employ black-box surrogates evaluated on short, static scenarios, they rarely exploit internal controller specifications or assess transferability to reactive closed-loop environments. In this paper, we aim to investigate how SA-SBT can be applied to a robot controller based on Enzymatic Numerical P systems (ENPS). We build and evaluate two surrogate strategies: a black-box classifier using road geometry and ENPS model identifier and a gray-box surrogate reconstructed directly from the controller internal rules. The classifier substantially reduces the number of required simulations while retaining nearly all true failures on independently generated test roads. In addition, we present and evaluate the gray-box surrogate, examining whether continuous fitness prediction, the mechanism underlying prior SA-SBT surrogates from the literature, transfers to a controller governed by a long, reactive control loop.
The authors' abstract, as published at the source. Applied Sciences, 2026 · DOI ↗
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Field: Software
SoftwareComputer Science