Metamorphic Testing of Vision-Language Action-Enabled Robots

Vision-Language-Action (VLA) models are multimodal robotic task controllers that, given an instruction and visual inputs, produce a sequence of low-level control actions (or motor commands) enabling a robot to execute the requested task in the physical environment. These systems face the test oracle problem from multiple perspectives. On the one hand, a test oracle must be defined for each instruction prompt, which is a complex and non-generalizable approach. On the other hand, current state-of-the-art oracles typically capture symbolic representations of the world (e.g., robot and object states), enabling the correctness evaluation of a task, but fail to assess other critical aspects, such as the quality with which VLA-enabled robots perform a task. In this paper, we explore whether Metamorphic Testing (MT) can alleviate the test oracle problem in this context. To do so, we propose two metamorphic relation patterns and five metamorphic relations to assess whether changes to the test inputs impact the original trajectory of the VLA-enabled robots. An empirical study involving five VLA models, two simulated robots, and four robotic tasks shows that MT can effectively alleviate the test oracle problem by automatically detecting diverse types of failures, including, but not limited to, uncompleted tasks. More importantly, the proposed MRs are generalizable, making the proposed approach applicable across different VLA models, robots, and tasks, even in the absence of test oracles.

UAMTERS: Uncertainty-Aware Mutation Analysis for DL-enabled Robotic Software

Self-adaptive robots adjust their behaviors in response to unpredictable environmental changes. These robots often incorporate deep learning (DL) components into their software to support functionality such as perception, decision-making, and control, enhancing autonomy and self-adaptability. However, the inherent uncertainty of DL-enabled software makes it challenging to ensure its dependability in dynamic environments. Consequently, test generation techniques have been developed to test robot software, and classical mutation analysis injects faults into the software to assess the test suite's effectiveness in detecting the resulting failures. However, there is a lack of mutation analysis techniques to assess the effectiveness under the uncertainty inherent to DL-enabled software. To this end, we propose UAMTERS, an uncertainty-aware mutation analysis framework that introduces uncertainty-aware mutation operators to explicitly inject stochastic uncertainty into DL-enabled robotic software, simulating uncertainty in its behavior. We further propose mutation score metrics to quantify a test suite's ability to detect failures under varying levels of uncertainty. We evaluate UAMTERS across three robotic case studies, demonstrating that UAMTERS more effectively distinguishes test suite quality and captures uncertainty-induced failures in DL-enabled software.

Assessing Vision-Language Models for Perception in Autonomous Underwater Robotic Software

Autonomous Underwater Robots (AURs) operate in challenging underwater environments, including low visibility and harsh water conditions. Such conditions present challenges for software engineers developing perception modules for the AUR software. To successfully carry out these tasks, deep learning has been incorporated into the AUR software to support its operations. However, the unique challenges of underwater environments pose difficulties for deep learning models, which often rely on labeled data that is scarce and noisy. This may undermine the trustworthiness of AUR software that relies on perception modules. Vision-Language Models (VLMs) offer promising solutions for AUR software as they generalize to unseen objects and remain robust in noisy conditions by inferring information from contextual cues. Despite this potential, their performance and uncertainty in underwater environments remain understudied from a software engineering perspective. Motivated by the needs of an industrial partner in assurance and risk management for maritime systems to assess the potential use of VLMs in this context, we present an empirical evaluation of VLM-based perception modules within the AUR software. We assess their ability to detect underwater trash by computing performance, uncertainty, and their relationship, to enable software engineers to select appropriate VLMs for their AUR software.

Out of Distribution Detection in Self-adaptive Robots with AI-powered Digital Twins

Self-adaptive robots (SARs) in complex, uncertain environments must proactively detect and address abnormal behaviors, including out-of-distribution (OOD) cases. To this end, digital twins offer a valuable solution for OOD detection. Thus, we present a digital twin-based approach for OOD detection (ODiSAR) in SARs. ODiSAR uses a Transformer-based digital twin to forecast SAR states and employs reconstruction error and Monte Carlo dropout for uncertainty quantification. By combining reconstruction error with predictive variance, the digital twin effectively detects OOD behaviors, even in previously unseen conditions. The digital twin also includes an explainability layer that links potential OOD to specific SAR states, offering insights for self-adaptation. We evaluated ODiSAR by creating digital twins of two industrial robots: one navigating an office environment, and another performing maritime ship navigation. In both cases, ODiSAR forecasts SAR behaviors (i.e., robot trajectories and vessel motion) and proactively detects OOD events. Our results showed that ODiSAR achieved high detection performance -- up to 98\% AUROC, 96\% TNR@TPR95, and 95\% F1-score -- while providing interpretable insights to support self-adaptation.

Evaluating Uncertainty and Quality of Visual Language Action-enabled Robots

Visual Language Action (VLA) models are a multi-modal class of Artificial Intelligence (AI) systems that integrate visual perception, natural language understanding, and action planning to enable agents to interpret their environment, comprehend instructions, and perform embodied tasks autonomously. Recently, significant progress has been made to advance this field. These kinds of models are typically evaluated through task success rates, which fail to capture the quality of task execution and the mode's confidence in its decisions. In this paper, we propose eight uncertainty metrics and five quality metrics specifically designed for VLA models for robotic manipulation tasks. We assess their effectiveness through a large-scale empirical study involving 908 successful task executions from three state-of-the-art VLA models across four representative robotic manipulation tasks. Human domain experts manually labeled task quality, allowing us to analyze the correlation between our proposed metrics and expert judgments. The results reveal that several metrics show moderate to strong correlation with human assessments, highlighting their utility for evaluating task quality and model confidence. Furthermore, we found that some of the metrics can discriminate between high-, medium-, and low-quality executions from unsuccessful tasks, which can be interesting when test oracles are not available. Our findings challenge the adequacy of current evaluation practices that rely solely on binary success rates and pave the way for improved real-time monitoring and adaptive enhancement of VLA-enabled robotic systems.

Search-Based Software Engineering in the Landscape of AI Foundation Models

Search-based software engineering (SBSE), at the intersection of artificial intelligence (AI) and software engineering, has been an active area of research for about 25 years. It has been applied to solve numerous problems across the entire software engineering lifecycle and has demonstrated its versatility in multiple domains. With the recent advancements in AI, particularly the emergence of foundation models (FMs), the evolution of SBSE alongside FMs remains undetermined. In this window of opportunity, we propose a research roadmap that articulates the current landscape of SBSE in relation to foundation models (FMs), highlights open challenges, and outlines potential research directions for advancing SBSE through its interplay with FMs. This roadmap aims to establish a forward-thinking and innovative perspective for the future of SBSE in the era of FMs.

Software Engineering for Self-Adaptive Robotics: A Research Agenda

Self-adaptive robotic systems are designed to operate autonomously in dynamic and uncertain environments, requiring robust mechanisms to monitor, analyse, and adapt their behaviour in real-time. Unlike traditional robotic software, which follows predefined logic, self-adaptive robots leverage artificial intelligence, machine learning, and model-driven engineering to continuously adjust to changing operational conditions while ensuring reliability, safety, and performance. This paper presents a research agenda for software engineering in self-adaptive robotics, addressing critical challenges across two key dimensions: (1) the development phase, including requirements engineering, software design, co-simulation, and testing methodologies tailored to adaptive robotic systems, and (2) key enabling technologies, such as digital twins, model-driven engineering, and AI-driven adaptation, which facilitate runtime monitoring, fault detection, and automated decision-making. We discuss open research challenges, including verifying adaptive behaviours under uncertainty, balancing trade-offs between adaptability, performance, and safety, and integrating self-adaptation frameworks like MAPE-K. By providing a structured roadmap, this work aims to advance the software engineering foundations for self-adaptive robotic systems, ensuring they remain trustworthy, efficient, and capable of handling real-world complexities.

Identifying Uncertainty in Self-Adaptive Robotics with Large Language Models

Future self-adaptive robots are expected to operate in highly dynamic environments while effectively managing uncertainties. However, identifying the sources and impacts of uncertainties in such robotic systems and defining appropriate mitigation strategies is challenging due to the inherent complexity of self-adaptive robots and the lack of comprehensive knowledge about the various factors influencing uncertainty. Hence, practitioners often rely on intuition and past experiences from similar systems to address uncertainties. In this article, we evaluate the potential of large language models (LLMs) in enabling a systematic and automated approach to identify uncertainties in self-adaptive robotics throughout the software engineering lifecycle. For this evaluation, we analyzed 10 advanced LLMs with varying capabilities across four industrial-sized robotics case studies, gathering the practitioners' perspectives on the LLM-generated responses related to uncertainties. Results showed that practitioners agreed with 63-88% of the LLM responses and expressed strong interest in the practicality of LLMs for this purpose.

Digital Twin-based Out-of-Distribution Detection in Autonomous Vessels

An autonomous vessel (AV) is a complex cyber-physical system (CPS) with software enabling many key functionalities, e.g., navigation software enables an AV to autonomously or semi-autonomously follow a path to its destination. Digital twins of such AVs enable advanced functionalities such as running what-if scenarios, performing predictive maintenance, and enabling fault diagnosis. Due to technological improvements, real-time analyses using continuous data from vessels' real-time operations have become increasingly possible. However, the literature has little explored developing advanced analyses in real-time data in AVs with digital twins built with machine learning techniques. To this end, we present a novel digital twin-based approach (ODDIT) to detect future out-of-distribution (OOD) states of an AV before reaching them, enabling proactive intervention. Such states may indicate anomalies requiring attention (e.g., manual correction by the ship master) and assist testers in scenario-centered testing. The digital twin consists of two machine-learning models predicting future vessel states and whether the predicted state will be OOD. We evaluated ODDIT with five vessels across waypoint and zigzag maneuvering under simulated conditions, including sensor and actuator noise and environmental disturbances i.e., ocean current. ODDIT achieved high accuracy in detecting OOD states, with AUROC and TNR@TPR95 scores reaching 99\% across multiple vessels.

A Machine Learning-Based Error Mitigation Approach For Reliable Software Development On IBM'S Quantum Computers

Quantum computers have the potential to outperform classical computers for some complex computational problems. However, current quantum computers (e.g., from IBM and Google) have inherent noise that results in errors in the outputs of quantum software executing on the quantum computers, affecting the reliability of quantum software development. The industry is increasingly interested in machine learning (ML)--based error mitigation techniques, given their scalability and practicality. However, existing ML-based techniques have limitations, such as only targeting specific noise types or specific quantum circuits. This paper proposes a practical ML-based approach, called Q-LEAR, with a novel feature set, to mitigate noise errors in quantum software outputs. We evaluated Q-LEAR on eight quantum computers and their corresponding noisy simulators, all from IBM, and compared Q-LEAR with a state-of-the-art ML-based approach taken as baseline. Results show that, compared to the baseline, Q-LEAR achieved a 25% average improvement in error mitigation on both real quantum computers and simulators. We also discuss the implications and practicality of Q-LEAR, which, we believe, is valuable for practitioners.