Datasets › SecurityEval
SecurityEval
Automated source code generation is currently a popular machine learning-based task. It can be helpful for software developers to write functionally correct code from a given context. However, just like human developers, a code generation model can produce vulnerable code, which the developers can mistakenly use. For this reason, evaluating the security of a code generation model is a must. In this paper, we describe SecurityEval, an evaluation dataset to fulfill this purpose. It contains 130 samples for 75 vulnerability types, which are mapped to the Common Weakness Enumeration (CWE). We also demonstrate using our dataset to evaluate one open-source (i.e., InCoder) and one closed-source code generation model (i.e., GitHub Copilot).
Benchmarks archive 2025-07-28
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Papers archive 2025-07-28
No paper in the archive has a leaderboard row on this dataset; the archive counts 17 papers for it but never published that list.
Dataset loaders archive 2025-07-28
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Tasks archive 2025-07-28
License archive 2025-07-28
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Modalities archive 2025-07-28
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Languages archive 2025-07-28
Variants archive 2025-07-28
- SecurityEval
1 variant name, as the archive lists them.
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