From Coverage Computation to Fault Localization: A Generic Framework for Domain-Specific Languages
In: 15th ACM SIGPLAN International Conference on Software Language Engineering (SLE 2022) 15th ACM SIGPLAN International Conference on Software Language Engineering (SLE 2022), Dec 2022, Auckland, New Zealand. ⟨10.1145/3567512.3567532⟩ Proceedings of the 15th ACM SIGPLAN International Conference on Software Language Engineering SLE 2022: 15th ACM SIGPLAN International Conference on Software Language Engineering SLE 2022: 15th ACM SIGPLAN International Conference on Software Language Engineering, Dec 2022, Auckland, New Zealand. pp.235-248, ⟨10.1145/3567512.3567532⟩; (2022-12-06)
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International audience; To test a system efficiently, we need to know how good are the defined test cases and to localize detected faults in the system. Measuring test coverage can address both concerns as it is a popular metric for test quality evaluation and, at the same time, is the foundation of advanced fault localization techniques. However, for Domain-Specific Languages (DSLs), coverage metrics and associated tools are usually manually defined for each DSL representing costly, error-prone, and non-reusable work. To address this problem, we propose a generic coverage computation and fault localization framework for DSLs. Considering a test suite executed on a model conforming to a DSL, we compute a coverage matrix based on three ingredients: the DSL specification, the coverage rules, and the model's execution trace. Using the test execution result and the computed coverage matrix, the framework calculates the suspiciousness-based ranking of the model's elements based on existing spectrum-based techniques to help the user in localizing the model's faults. We provide a tool atop the Eclipse GEMOC Studio and evaluate our approach using four different DSLs, with 297 test cases for 21 models in total.
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From Coverage Computation to Fault Localization: A Generic Framework for Domain-Specific Languages
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Autor/in / Beteiligte Person: | Khorram, Faezeh ; Bousse, Erwan ; Garmendia, Antonio ; Mottu, Jean-Marie ; Sunyé, Gerson ; Wimmer, Manuel ; NaoMod - Nantes Software Modeling Group (LS2N - équipe NaoMod) ; Laboratoire des Sciences du Numérique de Nantes (LS2N) ; Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique) ; Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-École Centrale de Nantes (Nantes Univ - ECN) ; Nantes Université (Nantes Univ)-Nantes Université (Nantes Univ)-Nantes université - UFR des Sciences et des Techniques (Nantes univ - UFR ST) ; Nantes Université - pôle Sciences et technologie ; Nantes Université (Nantes Univ)-Nantes Université (Nantes Univ)-Nantes Université - pôle Sciences et technologie ; Nantes Université (Nantes Univ)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique) ; Nantes Université (Nantes Univ) ; Département Automatique, Productique et Informatique (IMT Atlantique - DAPI) ; IMT Atlantique (IMT Atlantique) ; Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT) ; Johannes Kepler University Linz [Linz] (JKU) ; European Project: 813884,H2020-EU.1.3.1.,H2020-MSCA-ITN-2018,Lowcomote(2019) ; Johannes Kepler Universität Linz - Johannes Kepler University Linz [Autriche] (JKU) |
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Quelle: | 15th ACM SIGPLAN International Conference on Software Language Engineering (SLE 2022) 15th ACM SIGPLAN International Conference on Software Language Engineering (SLE 2022), Dec 2022, Auckland, New Zealand. ⟨10.1145/3567512.3567532⟩ Proceedings of the 15th ACM SIGPLAN International Conference on Software Language Engineering SLE 2022: 15th ACM SIGPLAN International Conference on Software Language Engineering SLE 2022: 15th ACM SIGPLAN International Conference on Software Language Engineering, Dec 2022, Auckland, New Zealand. pp.235-248, ⟨10.1145/3567512.3567532⟩; (2022-12-06) |
Veröffentlichung: | HAL CCSD, 2022 |
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