Editing AI for Code Generation (section)

== <span style="color: #FFFFFF;">Analyzing</span> ==
{| class="wikitable"
|+ Code Generation Model Comparison (2024)
! Model !! HumanEval (pass@1) !! SWE-bench Verified !! Context !! Best For
|-
| GPT-4o || ~90% || ~49% || 128k tokens || General coding, complex reasoning
|-
| Claude 3.5 Sonnet || ~92% || ~49% || 200k tokens || Long files, agentic coding
|-
| DeepSeek-Coder-V2 || ~90% || ~38% || 128k tokens || Open source, local deployment
|-
| Gemini 1.5 Pro || ~87% || ~35% || 1M tokens || Very long codebase context
|-
| Llama 3.1 405B || ~89% || N/A || 128k tokens || Open source, customizable
|}

'''Failure modes:'''
* '''Plausible but incorrect code''' — Generated code looks correct syntactically but has subtle logical errors. The greatest danger: code that passes obvious tests but fails edge cases.
* '''Security vulnerabilities''' — Models reproduce insecure patterns from training data: SQL injection, buffer overflows, hardcoded credentials, improper input validation.
* '''API hallucination''' — Models confidently call functions or methods that don't exist in the library, or use outdated API signatures.
* '''Context window limits''' — For large codebases, the model cannot see all relevant files simultaneously. It may generate code inconsistent with unseen parts of the codebase.
* '''Test gaming''' — An agent told to "make the tests pass" might delete the tests or hardcode expected outputs rather than implementing the actual logic.
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