Core development for static analysis tooling and C/C++-oriented infrastructure.

C & C++ are central to my work on static-analysis-related tooling and infrastructure. They are also the main target languages of the analysis itself, especially in the context of safety-critical coding standards and large industrial codebases.

Systems development and prototyping for isolated developer tools, report access, command-line interfaces, and AI integrations.

Rust is used for building standalone tooling around ECLAIR, including command-line and protocol-based interfaces for structured access to analysis data. Its strong type system, performance, deployment model, and support for reliable systems programming make it suitable for isolated components that need to evolve rapidly without becoming tightly coupled to existing applications.

Production MCP integration connecting language-model clients to static-analysis results through structured tools.

I designed and implemented ECLAIR's MCP integration in Rust, from an early prototype to release. It provides AI clients with structured access to static-analysis databases, source excerpts, manuals, and external project documents. The implementation includes protocol-level fixture tests and a distributable self-test harness.

Logic programming for expressing rules, constraints, and analysis knowledge.

Prolog is useful for representing declarative relationships, rules, and constraints. In a static analysis context, this style is valuable because many checks can be naturally described in terms of facts, relations, and logical conditions.

Analysis of source code to detect defects, enforce rules, and support compliance.

Static analysis is the main technical domain of my current professional work. It combines programming language knowledge, software engineering, formal reasoning, and practical tooling to inspect programs without executing them.

MISRA, CERT, AUTOSAR, and related rule-based software quality requirements.

Safety-critical coding standards define restrictions and guidelines intended to improve reliability, portability, maintainability, and analyzability of software. My work is connected to tooling that supports these standards for C and C++ projects.

Scripting, automation, data processing, and small development tools.

Python is useful for automation, glue code, experiments, data processing, and small internal tools around larger software systems.

Scripting and automation in tooling contexts.

Lua is a lightweight scripting language that can be useful in tooling, configuration, and automation contexts. It is used in specific tools where embedding a small scripting engine and a reduced runtime requirements is beneficial for flexibility and extensibility.

Frontend and web-related development around developer tools.

JavaScript and related web technologies are useful for building interfaces, documentation, dashboards, and developer-facing tools.

IDE-related tooling and integration work.

Java is relevant for tooling and integration work, especially in contexts involving IDE plugins and development environments. I only occasionally work with Java in this context, but it is part of the technology stack for certain integrations.

Containerization and deployment.

Docker is used to create reproducible environments, CI/CD pipelines, and other deployment workflows.

Application and algorithm development for core algorithms and application logic, including some legacy codebases.

Image processing and measurement-oriented vision algorithms.

Practical work with electronics, cameras, projectors, mechanics, and 3D printing.