Dynamically Instrumenting the QEMU Emulator for Linux Process Trace Generation with the GDB Debugger
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In software debugging, trace generation techniques are used to resolve highly complex bugs. However, the emulators increasingly used for embedded software development do not yet offer the types of trace generation infrastructure available in hardware. In this article, we make changes to the ARM ISA emulation of the QEMU emulator to allow for continuous instruction-level trace generation. Using a standard GDB client, tracepoints can be inserted to dynamically log registers and memory addresses without altering executing code. The ability to run trace experiments in five different modes allows the scope of trace generation to be narrowed as needed, down to the level of a single Linux process. Our scheme collects the execution traces of a Linux process on average between 9.6x--0.7x the speed of existing QEMU trace capabilities, with 96.7% less trace data volume. Compared to a software-instrumented tracing scheme, our method is both unobtrusive and performs on average between 3--4 orders of magnitude faster.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it