Defensive-code and over-engineering signatures
Parent: LLM tells in code, commit messages, comments and READMEs · Published reference · snapshot 2026-09-23
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Source-anchored research for Defensive-code and over-engineering signatures. Run status: COMPLETED.
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Structure and components
- Practitioner signature list of AI-grown bloat: verbose scaffolding layers, partial fixes, ceremonial state layers mirroring other layers, non-functional placeholder subsystems left wired in, debug harness code in shipping source, and defensive abstractions that accumulate each agent turn; one Flutter app shrank 31.7% with tests passing. (low confidence) (verified as of 2026-06-01) [source]
Measurements and reference values
- Across four LLMs, generated code had on average 63.34% more code smells than reference solutions (73.35% implementation smells, 21.42% design smells), with larger increases on more complex tasks. (low confidence) [source]
Problems, failure modes and limitations
- Search-result snippets (not fetched) report opposite security-study findings: LLM code lacking defensive constructs, and one model with error handling in only 3.3% of scripts. Excess defensiveness is not established as a general LLM tell. (low confidence) [source]
Comparisons and alternatives
- GitClear's analysis of 211M changed lines (2020-2024) reports copy/pasted lines rising from 8.3% to 12.3% of changed lines while moved (refactored) lines fell from 25% to under 10%, read as assistants adding new code rather than reusing existing functions. (low confidence) (verified as of 2026-01-01) [source]
Facts and statements
- Agent-generated code shows procedural bloat (Long Method dominant) on simple tasks and architectural decay (God Class, superficial file separation) in complex systems; total lines of code correlates with architectural degradation at rho=0.94. (medium confidence) [source]
- More capable models can produce messier code as they try to handle every edge case, yielding excessively long methods; detailed prompts and few-shot examples did not reduce defects in that study. (low confidence) [source]
Children
- No children recorded.