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Alvaro Serero

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LLM tooling at Thales SIX GTS

AI Software Engineer Intern · embedded software team for defence aeronautics radiocommunications

Period
Apr – Sep 2026
My part
Sole developer, end to end, of both tools: design, implementation, benchmarking, CI/CD, packaging, documentation and deployment.
Stack
  • Python
  • Typer
  • NiceGUI
  • Pydantic
  • pytest
  • mypy
  • ruff
  • Jenkins
  • Docker
  • Mistral models via an OpenAI-compatible API

Sole developer of two LLM tools for defence avionics software: a requirements-review analyser benchmarked at up to 93% agreement with human experts, and prbot, an automated pull-request review CLI adopted as the team's implementation.

Context

Software under DO-178C, the airborne software certification standard, has to be traceable, reviewable and justifiable line by line. That shaped every technical decision.

EARS Requirements Analyser

  • A Python tool (CLI + web UI) that reviews software requirements documents before formal peer review. It grades every requirement against the 6 EARS syntax patterns and 5 writing-quality criteria using Thales' internal LLM gateway, and proposes corrections.
  • Built the benchmark behind it: a ~100-requirement human-annotated ground truth and an asymmetric penalty scheme that treats a missed defect as worse than a false alarm. Evaluated 6 LLMs across 5 batch sizes and reached up to 93% agreement with human experts. Found a batch-size trade-off in LLM grading (false negatives rise as false positives fall) that informed production configuration.

prbot

  • A Python CLI for LLM-powered code review on pull requests, with inline comments anchored to exact diff lines, ticket context and a strict JSON output contract. It consolidated 3 existing prototypes into the team's adopted implementation.

Shipping both

  • ~380 tests, Jenkins CI/CD, wheel packaging, versioned prompts traceable in every report, and a pipeline stage engineered to never break the team's build.