Developing drivers for undocumented hardware is traditionally a labor-intensive process that requires capturing raw bus traffic, manually inspecting hex dumps, and forming hypotheses about register behaviors—a cycle that often takes weeks or months of patient trial and error. Christian F.K. Schaller has demonstrated that this 'slog' can be significantly streamlined using AI agents like Claude. By providing an AI model with access to thousands of lines of USB capture data, it can identify repeating sequences where a single byte changes, propose what those bytes represent, and generate test scripts to confirm the findings on real hardware.
The methodology involves creating a comparison harness to establish a baseline for successful operation. For devices with accessible buses, such as scanners, the workflow includes running a vendor’s Windows driver within a virtual machine while recording the resulting traffic from the host OS via tools like `usbmon`. This creates a reference trace. The developer then executes similar operations on Linux using a nascent custom driver, captures a second trace, and uses the AI to diff the two. Because both traces originate from the same physical instrument, any discrepancies in the captured data highlight specific bugs or missing features in the new driver.
This image illustrates the comparison harness methodology where a vendor driver and a custom driver are compared.
Schaller applied this approach to several projects, including developing a SANE backend (the standard Linux subsystem for scanners) for a Plustek OpticFilm 8200i and fixing MIPI camera issues on a Lenovo ThinkPad X1 Carbon. In the latter case, the AI helped identify kernel-level crop selection bugs and incorrect register widths that caused severe color distortion and vignetting. While the human remains responsible for holding the physical hardware and verifying final results, the AI handles the heavy lifting of decoding captures and cross-checking constants. However, limitations remain: there is no substitute for 'real' hardware testing, and some internal components—like those without an external bus to tap—require more complex inspection of INF files, registry data, and ACPI tables.
This image demonstrates the specific results of the AI-assisted debugging process for the Lenovo ThinkPad X1 Carbon camera.