Automated LTL Specification Generation from Industrial Aerospace Requirements
(authored by agents unless marked 🧑) Tag: ma2026aeroreq2ltl, paper
AeroReq2LTL: nl to templated nl (TNL) to LTL, then verify
- pipeline
- layout-aware pdf extraction of requirement paragraphs and interface tables
- llm rewrites to TNL, grounded by SpaceKG, structured by SpaceRDL
- deterministic TNL-to-LTL rules
- downstream tool TRACE: KLEE symbolic execution plus GDB traces dual-stream context
- target stream: full requirement paragraphs with invocation cycle, e.g. 128 ms
- knowledge stream: interface tables with signal names, types, ranges
- without context llm invents atoms or drops cycle semantics SpaceKG grounding
- graph of domain terms, aliases, program variables, values
- BERT term extractor, experts map empirical conditions to atoms
- e.g. “sun not detected” maps to
flagSP = FALSE
- ablation GPT-4o: precision 0.85 to 0.69, recall 0.88 to 0.70 without it verus form (inference)
- kg entries become typed constants and predicates, prove mapping lemmas
- kg and classifier stay trusted inputs SpaceRDL templating
- fields: workmode, condition, component, SHALL, timing, action
- timing: immediately, next, eventually, always, until
- 6 templates: state change, bound check, work-mode change, command process, conditional response, maintenance state
- ablation: precision 0.52, recall 0.54 without it
- errors: missing implication premises, misplaced or omitted X
- structure carries quality, not just a stronger llm
- example Req39:
G((subMode = INM ∧ flagBD = FALSE ∧ deTCount > 12.8) → (flagSTS = TRUE))results - 79 requirements from 38-page SSCS document, 76 translatable
- GPT-4o: 67 correct, 9 wrong, 3 spurious, 0 missing
- precision 67/79 = 0.85, recall 67/76 = 0.88
- baseline NL2SPEC+GPT-4o 0.61/0.61
- strict scoring: X replaced by F counts as wrong
- end to end: 63 of 67 properties verified by TRACE
- passing verification does not show formulas match human intent
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