\head{Background} Task-specialized language models are increasingly integrated into software engineering workflows to support vertical-domain activities such as issue triaging, document classification, and automated analysis. Despite their adoption, there is limited empirical evidence on how to test their robustness and detect brittle behaviors under semantics-preserving input transformations. \head{Aims} This paper investigates whether explainability-guided metamorphic testing can improve the effectiveness and validity of robustness testing for specialized language models compared to heuristic mutation strategies. \head{Method} We conduct a large-scale empirical study of explanation-guided metamorphic testing across three datasets, four model architectures, and 20 testing configurations derived from combinations of attribution methods and mutation strategies. The evaluated configurations combine attribution-based token prioritization, LLM-driven mutation, and automated semantic verification to generate linguistically valid test variants. We assess failure discovery capability, semantic validity, and testing efficiency against heuristic baselines. \head{Results} Explanation-guided metamorphic testing generates 2.30$\times$ more verified failure-inducing test cases than heuristic mutation strategies. Semantic verification substantially improves mutation validity and achieves high label-preservation precision among gate-accepted variants according to human annotation. The study further reveals systematic shortcut behaviors across models, including over-reliance on named entities and formatting cues. \head{Conclusions} The results provide evidence that explanation-guided metamorphic testing is an effective and practical approach for empirically evaluating the robustness of task-specialized language models used in vertical AI applications.
Xingcheng Chen, Mehmet Besenk, Andrea Stocco· 0 citations
Automatic speech recognition (ASR) systems have achieved high accuracy with transformer-based models, enabling deployment in critical applications. However, they remain vulnerable to adversarial manipulation, particularly in black-box settings where attacks must preserve perceptual naturalness. This work introduces GATAS, a black-box testing approach that generates failure inducing inputs by operating in the phoneme-level latent space of a text- to-speech model. Instead of perturbing waveforms directly, the approach interpolates latent representations to induce transcription errors while remaining within the manifold of natural speech. The attack is formulated as a multi-objective optimization problem balancing semantic divergence and perceptual quality. Our empirical evaluation against both white-box and black-box baselines shows that GATAS achieves a 98% success rate while producing lower distortion and higher perceptual quality, as confirmed by human studies. Despite operating without gradient access, GATAS remains competitive against white-box methods, highlighting that representation and perceptual alignment are more critical than access to model internals. Overall, our results demonstrate that untargeted latent-space optimization enables the efficient generation of realistic and effective test cases for ASR systems.
Yanis Xabier Wilbrand Pena, Oliver Weissl, Andrea Stocco· 0 citations
Autonomous driving research has largely focused on safety while giving limited attention to non-functional aspects such as energy consumption and sustainability. As Autonomous Electric Vehicles (AEVs) become increasingly common in urban traffic, understanding how complex traffic dynamics influence their energy consumption is paramount to test whether AEVs can complete trips before battery depletion. To support energy-aware scenario-based testing of AEVs, we present E-CoDrive, a framework for reproducible closed-loop driving co-simulations that integrates an energy consumption model, a micro-traffic simulator, and a high-fidelity driving simulator to test AEV software stacks in urban scenarios. This tool paper describes the architecture of E-CoDrive and demonstrates its applicability by testing an Autoware-based AEV stack. Our evaluation shows that varying traffic conditions produce substantial differences in vehicle energy consumption. The artifact is publicly available at https://doi.org/10.6084/m9.figshare.32244783, and a screencast showing the tool is available at https://youtu.be/yX9fWHqCvgc.
