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.
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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 | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".