Integrated modeling tool for indexing and analyzing state machine trace
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
It is important to model and understand an application or system runtime behavior to identify potential performance problems. Execution tracing, the basis of various dynamic analysis methods includes the collection of events, metrics, and statistics about the runtime behaviors of systems and applications. However, comprehensive execution tracing can result in very large trace files, most of which are irrelevant to the problem at hand. This is compounded by the inflexibility and complexity of common tools in how the user specifies what to capture, making the collection of relevant statistics difficult. While existing solutions allow for an adaptive collection of metrics and statistics, they often require users to write large and complex scripts in a domain-specific language. In this paper, we propose a state machine based modeling tool that simplifies the creation of user-defined and data-driven trace-based analyses. The proposed method combines advanced kernel-space and user-space execution trace events with powerful and adaptable modeling in order to automatically generating event-based analysis based on users’ specific requirements and problems. The difficulty and complexity of user-defined event tracing is drastically reduced. We demonstrate the efficiency, effectiveness, and simplicity of our proposed tool through real use cases of multi-level dynamic execution tracing in the Linux kernel.
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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 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