Symbolic model checking of product-line requirements using SAT-based methods
Bibliographic record
Abstract
Product line (PL) engineering promotes the development of families of related products, where individual products are differentiated by which optional features they include. Modelling and analyzing requirements models of PLs allows for early detection and correction of requirements errors -- including unintended feature interactions, which are a serious problem in feature-rich systems. A key challenge in analyzing PL requirements is the efficient verification of the product family, given that the number of products is too large to be verified one at a time. Recently, it has been shown how the high-level design of an entire PL, that includes all possible products, can be compactly represented as a single model in the SMV language, and model checked using the NuSMV tool. The implementation in NuSMV uses BDDs, a method that has been outperformed by SAT-based algorithms. In this paper we develop PL model checking using two leading SAT-based symbolic model checking algorithms: IMC and IC3. We describe the algorithms, prove their correctness, and report on our implementation. Evaluating our methods on three PL models from the literature, we demonstrate an improvement of up to 3 orders of magnitude over the existing BDD-based method.
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.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".