Feature specification and automated conflict detection
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
Large software systems, especially in the telecommunications field, are often specified as a collection of features. We present a formal specification language for describing features, and a method of automatically detecting conflicts ("undesirable interactions") amongst features at the specification stage. Conflict detection at this early stage can help prevent costly and time consuming problem fixes during implementation. Features are specified using temporal logic; two features conflict essentially if their specifications are mutually inconsistent under axioms about the underlying system behavior. We show how this inconsistency check may be performed automatically with existing model checking tools. In addition, the model checking tools can be used to provide witness scenarios, both when two features conflict as well as when the features are mutually consistent. Both types of witnesses are useful for refining the specifications. We have implemented a conflict detection tool, FIX (Feature Interaction eXtractor), which uses the model checker COSPAN for the inconsistency check. We describe our experience in applying this tool to a collection of telecommunications feature specifications obtained from the Telcordia (Bellcore) standards. Using FIX, we were able to detect most known interactions and some new ones, fully automatically, in a few hours processing time.
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 | 0.001 |
| 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