[2509.03242] TopoMap: A Feature-based Semantic Discriminator of the Topographical Regions in the Test Input Space

Computer Science > Machine Learning arXiv:2509.03242 (cs) [Submitted on 3 Sep 2025 (v1), last revised 24 Mar 2026 (this version, v2)] Title:TopoMap: A Feature-based Semantic Discriminator of the Topographical Regions in the Test Input Space Authors:Gianmarco De Vita, Nargiz Humbatova, Paolo Tonella View a PDF of the paper titled TopoMap: A Feature-based Semantic Discriminator of the Topographical Regions in the Test Input Space, by Gianmarco De Vita and 2 other authors View PDF Abstract:Testing Deep Learning (DL)-based systems is an open challenge. Although it is relatively easy to find inputs that cause a DL model to misbehave, the grouping of inputs by features that make the DL model under test fail is largely unexplored. Existing approaches for DL testing introduce perturbations that may focus on specific failure-inducing features, while neglecting others that belong to different regions of the feature space. In this paper, we create an explicit topographical map of the input feature space. Our approach, named TopoMap, is both black-box and model-agnostic as it relies solely on features that characterise the input space. To discriminate the inputs according to the specific features they share, we first apply dimensionality reduction to obtain input embeddings, which are then subjected to clustering. Each DL model might require specific embedding computations and clustering algorithms to achieve a meaningful separation of inputs into discriminative groups. We propose a n...