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We review machine learning approaches for detecting (and correcting) vulnerabilities in source code, finding that the biggest challenges ahead involve agreeing to a benchmark, increasing language and error type coverage, and using pipelines that do not flatten the code’s structure.
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Marjanov et al. (2022) studied this question.
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