Bibliographic record
Abstract
Dynamic analysis of real-world software systems is challenging due to imperfections, noise and data loss. Moreover, these systems evolve with time and their requirements are usually either not clearly specified or unknown, which makes it hard to analyze them. Therefore, it is important to create models that can learn to behave similarly to these systems to enable us to predict their actions, recover missing data, or detect potential failures ahead of time.Several models have been proposed to model sequential data, but the vast majority of them only have a qualitative notion of time or no notion of it at all. In this paper, we extend the work on incorporating a quantitative notion of time to RNN and introduce Time GRU. This modified GRU can learn the behaviour of complex software systems to a very high degree of accuracy. Our approach is scalable and has shown state-of-the-art performance on industry-strength software with real operating logs from Blackberry’s QNX real-time operating system. The proposed model can predict upcoming sequences of events more than 100 timesteps ahead in time with more than 90% accuracy. This allows for significant improvement in trace reconstruction and failure explainability.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".