MINTS: Unsupervised Temporal Specifications Miner
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
Specifications for software systems are quite often missing or are obsolete given the evolutionary nature of these systems. Lack of precise software specifications makes the task of debugging and detecting a malfunction of system behavior challenging. Prior works have primarily focused on extracting system specifications in the form of template-based mining frameworks or interactive simulation models. In safety-critical systems where the time of occurrence of events is of prime importance extracting specifications with a quantitative notion of time seems a daunting task. This work presents an unsupervised approach to mine timed temporal properties in the form of deterministic finite state machines with a custom-designed trie data structure. Our frame-work, MINTS learns dominant system specifications from their system traces that are represented as a timed deterministic finite state machine. MINTS is shown to be sound and complete. MINTS scalability and correctness is validated using real-world industry strength traces.
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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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