DevIntent: How Much Does LLM-Generated Code Violate Developer Intent?
arXiv 2608.07614•e49cdf08e8c66ee367b90e9fd1e7ba188a3887f26def244e992f0d6f48daff6a
AI code reliabilityLLM-generated codeagent safetyarXivautomated testingautomotive ADAScode correctnesscode reusecyber-physical systemsdeveloper intentdirected test generationfuzzingintent violationintrusion detectionlarge language modelsmodel-based systems engineeringmulti-agent systemsruntime feedbackself-evolving agentssoftware engineeringsoftware testingtest coverageverification
Paper metadata
- arXiv ID
- 2608.07614
- Version
- Not specified by this published record
- Category
- Computer Science — Software Engineering (cs.SE)
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Evidence and limitations
- Source ID
- arxiv_cs_se
- Record identifier
- e49cdf08e8c66ee367b90e9fd1e7ba188a3887f26def244e992f0d6f48daff6a
- Enrichment time
- 2026-08-11T08:51:40Z
- AI-assisted enrichment
- Yes
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