Berrios, Mario

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University of Córdoba, Spain

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  • Artículo
    Contrastive and counterfactual explanations for test case prioritization: Ideas and challenges
    Ramírez, Aurora; Berrios, Mario; Feldt, Robert; Romero, José Raúl. Actas de las XXVII Jornadas de Ingeniería del Software y Bases de Datos (JISBD 2023), 2023-09-12.
    As machine learning (ML) is increasingly used in software engineering (SE), explainable artificial intelligence (XAI) is crucial for understanding choices made by opaque, "black-box" models. Test case prioritization (TCP) is an important SE problem that can benefit from ML. In this paper, we explore two approaches for generating explanations in ML-based TCP, contrastive and counterfactual XAI, and present application scenarios where they can enhance testers' comprehension of model outputs. Specifically, we use DiCE, a method for generating counterfactual explanations, as an illustrative example and conclude by discussing open issues.
  • Artículo
    A methodology for explaining Learning-to-rank models for test case prioritization
    Berrios, Mario; Ramírez, Aurora; Feldt, Robert; Romero, José Raúl. Actas de las XXVIII Jornadas de Ingeniería del Software y Bases de Datos (JISBD 2024), 2024-06-17.
    In machine learning-based test case prioritization (TCP), there is a need for explainable methods that can shed light on the internal mechanisms of the models and clarify why certain test cases are deemed more likely to fail. To address this need, we propose an experimental methodology designed to generate and analyze global explanations for models in TCP. This methodology can help testers and researchers to understand how the impact of different features shifts as the software system evolves.