Mining Task Precedence Graphs from Real-Time Embedded System Traces
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
Real-time embedded systems have evolved from simple, self-contained single-processor computers to distributed multiprocessor systems that are extremely hard to develop and maintain. Execution tracing has proved itself to be a useful technology to gain a detailed knowledge of runtime behavior of software systems. However, the size and complexity of execution traces generated by modern embedded systems make manual trace analysis impossible. Therefore, software developers need tools to extract high-level system models from raw trace data. In this paper, we address the problem of mining task precedence graphs (TPG) from embedded system traces. A TPG can be helpful in performing several crucial software development and maintenance activities: understanding legacy systems, finding runtime bugs, and detect and diagnose anomalies in running systems. We rely on the recurrent nature of real-time systems to solve the TPG mining problem. We propose algorithms to train a TPG on a set of system traces, as well as an algorithm to detect anomalies in trace streams using a TPG. We evaluate our algorithms on industrial execution traces generated on production cars.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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