bfd-data-acquisition

Capture STM32 runtime data by resolving global symbols and memory regions from an ELF file, then sampling RAM or registers to generate structured JSON or CSV artifacts. The Python-based orchestration reads stack-published locals and RTT channels to produce machine-readable outputs for downstream analysis.

J-Link and ST-Link tooling are supported for STM32 RTT data acquisition. The Python-based orchestration interoperates with these debug probes to stream RTT channels and read memory regions, converting the captures into analyzable JSON and CSV artifacts.

Extract STM32F4 and STM32H7 stack-published locals into structured artifacts by running an acquisition workflow that resolves symbols from ELF metadata and samples runtime memory. This output-first workflow generates machine-readable JSON or CSV files with caches and summaries for downstream validation.

An ELF file or project metadata is required to resolve symbols for STM32 memory capture. The acquisition workflow relies on resolving these global symbols to accurately sample RAM, registers, and stack-published locals before converting the runtime state into structured artifacts.

Debug STM32 firmware by profiling runtime state and validating memory layouts through symbol-driven data acquisition. The workflow captures RTT streams and memory region reads, producing cached JSON and CSV artifacts that enable detailed analysis of the target's runtime behavior.

Avoid using RTT channels for STM32 runtime data acquisition when your target lacks RTT support or you only need static memory snapshots. In these cases, rely solely on direct memory region reads and global symbol resolution from the ELF file to generate the structured artifacts.