Editing AI for Code Generation (section)

== <span style="color: #FFFFFF;">Evaluating</span> ==
Expert evaluation of code generation systems:

'''pass@k metric''': Sample k code solutions, run all unit tests, and measure the probability at least one passes: pass@k = 1 - C(n-c, k)/C(n, k), where n=total samples, c=passing samples. pass@1 (single best attempt) is the production-relevant metric; pass@10 or pass@100 measures model capability.

'''SWE-bench''': A far more realistic benchmark than HumanEval. Given a real GitHub issue description and repository, can the model write a patch that fixes the issue (validated by the repository's test suite)? SWE-bench Verified has human-validated problem statements, making it more reliable.

'''Security evaluation''': CyberSecEval (Meta) and SVEN benchmark test whether models generate vulnerable code or can identify security issues. Code generation systems for production use must be evaluated here.

'''Practical utility beyond accuracy''': Does the AI reduce developer time? Does it generate code that needs significant editing? Does it produce maintainable, idiomatic code or technically correct but unreadable code? User surveys and time-to-completion studies supplement automated metrics.

Expert practitioners run generated code through static analysis (Bandit, Semgrep, pylint) and require generated code to pass the same code review standards as human-written code.
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