facebookresearch / CodeGen
Reference implementation of code generation projects from Facebook AI Research. General toolkit to apply machine learning to code, from dataset creation to model training and evaluation. Comes with pretrained models.
Summary
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| Main Code: 94,368 LOC (2111 files) = JAVA (37%) + PY (36%) + CPP (25%) + HTML (<1%) + PERL (<1%) + JSONL (<1%) Secondary code: Test: 4,970 LOC (62); Generated: 0 LOC (0); Build & Deploy: 506 LOC (15); Other: 817 LOC (12); |
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| Duplication: 14% | |||
| File Size: 1% long (>1000 LOC), 87% short (<= 200 LOC) | |||
| Unit Size: 4% long (>100 LOC), 45% short (<= 10 LOC) | |||
| Conditional Complexity: 4% complex (McCabe index > 50), 75% simple (McCabe index <= 5) | |||
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Logical Component Decomposition: primary (18 components) | ||
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6 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) | |||
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Features of interest:
TODOs
12 files |
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Latest commit date: 2022-01-24
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6
commits
(30 days)
2
contributors
(30 days) |
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