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README.md
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We have created a new version of the benchmark with instances that are harder than the previous one. There has been a lot of progress in models
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over the last year as a result the previous version of the benchmark was saturated. The methodology is the same, we have also released the
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dataset generation script which scans the top 100 Python projects to generate the instances. You can see it [here]().
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works as before. You do not need to login to Semgrep anymore as we
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The highest score a model can get on this benchmark is 100%, you can see the oracle run logs [here](https://huggingface.co/datasets/patched-codes/static-analysis-eval/blob/main/oracle-0-shot_semgrep_1.85.0_20240820_174931.log).
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We have created a new version of the benchmark with instances that are harder than the previous one. There has been a lot of progress in models
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over the last year as a result the previous version of the benchmark was saturated. The methodology is the same, we have also released the
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dataset generation script which scans the top 100 Python projects to generate the instances. You can see it [here](https://huggingface.co/datasets/patched-codes/static-analysis-eval/blob/main/_script_for_gen.py).
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The same [eval script](https://huggingface.co/datasets/patched-codes/static-analysis-eval/blob/main/_script_for_eval.py) works as before. You do not need to login to Semgrep anymore as we
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only use their OSS rules for this version of the benchmark.
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The highest score a model can get on this benchmark is 100%, you can see the oracle run logs [here](https://huggingface.co/datasets/patched-codes/static-analysis-eval/blob/main/oracle-0-shot_semgrep_1.85.0_20240820_174931.log).
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