| openai / human-eval Code for the paper "Evaluating Large Language Models Trained on Code" |
 0.3K py JSONL 0.01K jsonl
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| Standard analyses: | Main Code: 289 LOC (7 files) = PY (97%) + JSONL (2%) Secondary code: Test: 0 LOC (0); Generated: 0 LOC (0); Build & Deploy: 22 LOC (1); Other: 3 LOC (1); |
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| Duplication: 0% | |||
| File Size: 0% long (>1000 LOC), 100% short (<= 200 LOC) | |||
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Logical Component Decomposition: primary (2 components) | ||
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4 years, 11 months old
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0% of code updated more than 50 times Also see temporal dependencies for files frequently changed in same commits. |
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| Goals: Keep the system simple and easy to change (4) | |||
| Experimental analyses (less reliable heuristic analyses): | |||
| Unit Size: 0% long (>100 LOC), 54% short (<= 10 LOC) | |||
| Conditional Complexity: 0% complex (McCabe index > 50), 77% simple (McCabe index <= 5) | |||
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Static Component Dependencies: primary (2 components) | ||
Overall Activity Per Year
Latest commit date: 2025-01-17
Reference analysis date: 2026-06-24
activity per month, week and day...Per MonthLatest commit date: 2025-01-17
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activity per month, week and day...Per MonthLatest commit date: 2025-01-17
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