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42 lines
1.5 KiB
Markdown
42 lines
1.5 KiB
Markdown
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# Error Handling in Pipeline Code
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## No silent swallowing
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Never write `except Exception: pass` or `except Exception: return`. Every except block must either:
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- Log at WARNING or higher with `exc_info=True`
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- Re-raise after cleanup
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- Return a clearly documented sentinel value with at least DEBUG logging
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The tracing code (`benchmarking/_tracing.py`) is the worst offender -- 7 bare excepts with zero logging. Don't add more.
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## Protect ast.parse() calls
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`ast.parse()` raises `SyntaxError` on malformed input. Always wrap it:
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```python
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try:
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tree = ast.parse(source)
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except SyntaxError:
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log.warning("Failed to parse %s", path)
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return None # or skip this file
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```
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Known unprotected calls: `_state.py:70`, `_ranking.py:33`.
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## XML/TOML parsing
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Always use `recover=True` for lxml XMLParser. Always wrap tomlkit.parse() in try/except. Log parsing failures at WARNING, not DEBUG -- config parsing failures matter.
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## Format consistency: client-server boundary
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The AI service returns markdown-fenced code blocks. Every endpoint response must be parsed with `CodeStringsMarkdown.parse_markdown_code()` before using the code. Currently only `/optimize` and `/optimize-line-profiler` do this correctly. The refinement, repair, and adaptive endpoints in `ai/_refinement.py` skip this step.
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Pattern to follow (from `pipeline/_candidate_gen.py:83-91`):
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```python
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parsed = CodeStringsMarkdown.parse_markdown_code(c.code)
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if not parsed.code_strings:
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continue
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plain_code = "\n\n".join(cs.code for cs in parsed.code_strings)
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```
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