Institution profile

University of Mondragon

Academic institutionnorthamerica · us
Official website
Research library11linked papers
Opportunities0open roles
Selected work

Representative Papers

Delta Debugging for Cyber-Physical Systems with Flaky Test Executions

Jul 28, 2026

This study addresses the challenge of reproducing failures in cyber-physical system (CPS) simulation testing, where non-deterministic behaviors often hinder consistent fault manifestation. To tackle this issue, the work extends delta debugging to stochastic CPS scenarios for the first time, introducing three novel delta debugging algorithms tailored for randomized environments. The proposed approach integrates statistical failure analysis, repeated execution, and environment-aware input minimization to identify a minimal triggering input while preserving fault semantics. Empirical evaluation on case studies involving elevator scheduling and autonomous mobile robots demonstrates that the method significantly enhances the stability of failure reproduction and substantially reduces debugging time, effectively mitigating execution flakiness inherent in such systems.

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RAG-TESTER: Automated End-to-End Testing of Retrieval-Augmented Large Language Models

Jul 26, 2026

This work addresses the lack of reliable end-to-end testing methods for Retrieval-Augmented Generation (RAG) systems, which stems from complex interactions among their components. The authors propose RagTester, the first automated evaluation framework that introduces a coverage-guided test generation strategy. RagTester systematically synthesizes test documents, queries, and expected answers, and leverages large language models as judges to detect failure modes such as inaccurate retrieval, unsupported answers, and insufficient context utilization. Evaluated across 24 configurations with 72,000 tests, RagTester identified 21,633 faults—6.6% more than baseline methods—and outperformed baselines in 20 out of 24 configurations, substantially enhancing pre-deployment validation capabilities for RAG systems.

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FATE-VLA:Failue-aware test generation for vision-language-action models

Jun 01, 2026

Existing static benchmarks struggle to effectively uncover sparse and clustered failures of vision-language-action (VLA) models in high-dimensional embodied spaces, leading to insufficient robustness evaluation. This work introduces active test generation into VLA assessment for the first time, proposing a failure-oriented dynamic testing framework that adaptively generates high-risk, diverse test cases through the synergy of diversity-guided scene exploration and an agent model trained on execution feedback. Experiments across four prominent VLA models demonstrate that this approach discovers 29.7% more failure cases on average—evidenced by, for instance, a drop in GR00T-N1.6’s success rate from 64.4% to 34.7%—significantly exposing model weaknesses and revealing richer failure modes, thereby advancing the evaluation paradigm toward active and dynamic methodologies.

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Recent publications

Latest Papers

Delta Debugging for Cyber-Physical Systems with Flaky Test Executions

Jul 28, 2026

This study addresses the challenge of reproducing failures in cyber-physical system (CPS) simulation testing, where non-deterministic behaviors often hinder consistent fault manifestation. To tackle this issue, the work extends delta debugging to stochastic CPS scenarios for the first time, introducing three novel delta debugging algorithms tailored for randomized environments. The proposed approach integrates statistical failure analysis, repeated execution, and environment-aware input minimization to identify a minimal triggering input while preserving fault semantics. Empirical evaluation on case studies involving elevator scheduling and autonomous mobile robots demonstrates that the method significantly enhances the stability of failure reproduction and substantially reduces debugging time, effectively mitigating execution flakiness inherent in such systems.

0 citationsRead paper

RAG-TESTER: Automated End-to-End Testing of Retrieval-Augmented Large Language Models

Jul 26, 2026

This work addresses the lack of reliable end-to-end testing methods for Retrieval-Augmented Generation (RAG) systems, which stems from complex interactions among their components. The authors propose RagTester, the first automated evaluation framework that introduces a coverage-guided test generation strategy. RagTester systematically synthesizes test documents, queries, and expected answers, and leverages large language models as judges to detect failure modes such as inaccurate retrieval, unsupported answers, and insufficient context utilization. Evaluated across 24 configurations with 72,000 tests, RagTester identified 21,633 faults—6.6% more than baseline methods—and outperformed baselines in 20 out of 24 configurations, substantially enhancing pre-deployment validation capabilities for RAG systems.

0 citationsRead paper

FATE-VLA:Failue-aware test generation for vision-language-action models

Jun 01, 2026

Existing static benchmarks struggle to effectively uncover sparse and clustered failures of vision-language-action (VLA) models in high-dimensional embodied spaces, leading to insufficient robustness evaluation. This work introduces active test generation into VLA assessment for the first time, proposing a failure-oriented dynamic testing framework that adaptively generates high-risk, diverse test cases through the synergy of diversity-guided scene exploration and an agent model trained on execution feedback. Experiments across four prominent VLA models demonstrate that this approach discovers 29.7% more failure cases on average—evidenced by, for instance, a drop in GR00T-N1.6’s success rate from 64.4% to 34.7%—significantly exposing model weaknesses and revealing richer failure modes, thereby advancing the evaluation paradigm toward active and dynamic methodologies.

0 citationsRead paper