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Identifying and selecting high-quality Metamorphic Relations (MRs) is a
challenge in Metamorphic Testing (MT). While some techniques for automatically
selecting MRs have been proposed, they are either domain-specific or rely on
strict assumptions about the applicability of a pre-defined MRs. This paper
presents a preliminary evaluation of MetaTrimmer, a method for selecting and
constraining MRs based on test data. MetaTrimmer comprises three steps:
generating random test data inputs for the SUT (Step 1), performing test data
transformations and logging MR violations (Step 2), and conducting manual
inspections to derive constraints (Step 3). The novelty of MetaTrimmer is its
avoidance of complex prediction models that require labeled datasets regarding
the applicability of MRs. Moreover, MetaTrimmer facilitates the seamless
integration of MT with advanced fuzzing for test data generation. In a
preliminary evaluation, MetaTrimmer shows the potential to overcome existing
limitations and enhance MR effectiveness.