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logseq/assets/SOSYM-23-00004493_Proof_hi_1692636004494_0.edn
2025-06-05 22:07:12 +02:00

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:content {:text "This easily leads to syntactic and semantic errors in transformations which are time-consuming to locate and fix. "},
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:content {:text "evolutionary algorithm (EA) approach to automatically repair transformations containing many semantic errors. "},
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:content {:text "To prevent the fitness plateaus and the single fitness peak limitations from our previous work, we include the notion of social diversity as an objective for our EA to promote repair patches tackling errors that are less covered by the other patches of the population. "},
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:content {:text "n this context, MDE sees models as first-class artifacts where domain-specific modeling languages to capture specific aspects of the solution."},
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:content {:text "when the transformation compiles but the implemented behavior is not the one that was intended by the developers, we say that it contains semantic errors."},
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:content {:text "Because semantic errors pertain to the transformations behavior and each faulty transformation needs tailored patches, predefined patches are not well-suited for semantic errors."},
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:content {:text "o fix errors related to a transformations behavior, automated approaches usually rely on a specification of the expected behavior (e.g., test cases or examples) to assess the fitness of a patch, and thus efficiently guide the search strategy."},
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:content {:text "utomated patch generation for fixing semantic errors in ATL transformation rules."},
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:content {:text "sing behavior specifications such as test cases to guide the search in EAs can exacerbate these limitations"},
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:content {:text "o improve the efficiency and effectiveness of EAs using test cases, our improved approach leverages the notion of social diversity [4]. "},
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:content {:text "e formulate the transformation repair as a multi-objective optimization problem, where solutions must optimize several objectives including social diversity. Our approach is implemented using the NSGA-II algorithm, a fast multi-objective EA [10]"},
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:content {:text "We tested each mutant with the AnAtlyser tool [8], which finds a wide range of syntactic errors(including type errors) in ATL transformations using static analysis."},
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:content {:text "We reused the approach presented in [42] to merge several mutants with one error to obtain mutants with several errors. "},
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:content {:text "the properties of input/output object,"},
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:content {:text "We identified nine kinds of elements that could be modified by an atomic edit operation:"},
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:content {:text "four sets with respectively two to five mutants and then merged them in each set to form four faulty transformation mutants with two to five semantic errors. "},
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:content {:text "n this experiment, we aim at testing social diversity with two configurations separately: as a crowding distance and as an objective"},
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:content {:text " a) without social diversity, b) with social diversity as a crowding distance, and c) with social diversity as an objective."},
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:content {:text "Note that in our earlier work [41], we only considered approach a), and applied it to two transformations."},
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:content {:text "To answer RQ1, we compare the effectiveness of each configuration, i.e., the number of times a run can find an optimal patch. "},
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:content {:text "o answer RQ2, we compare the efficiency of each configuration, i.e., the number of generations necessary for a run"},
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