When implementing algorithms for code analysis, type checking, or pattern matching, ensure they produce consistent and deterministic results for the same inputs. Edge cases should be explicitly handled rather than making assumptions.
When implementing algorithms for code analysis, type checking, or pattern matching, ensure they produce consistent and deterministic results for the same inputs. Edge cases should be explicitly handled rather than making assumptions.
Key practices:
Example of problematic code:
# Incorrect: Unconditionally reporting error without checking if a match was found
def check_overloads(call_site, overloads):
for overload in overloads:
if arity_matches(call_site, overload):
# Missing validation! We found a potential match but didn't verify it
pass
# Incorrectly assumes failure even when a match might exist
report_no_matching_overload(call_site)
Improved implementation:
# Correct: Explicitly track success and only report error when needed
def check_overloads(call_site, overloads):
potential_matches = []
for overload in overloads:
if arity_matches(call_site, overload):
potential_matches.append(overload)
# Only report error if truly no matches were found
if not potential_matches:
report_no_matching_overload(call_site)
else:
# Process potential matches further
validate_matches(call_site, potential_matches)
Non-deterministic algorithms can introduce subtle bugs that are difficult to reproduce and debug. By ensuring deterministic behavior and explicit validation of conditions, you can create more robust and maintainable code.
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