Applying Declarative Analysis to Software Product Line Models: An\n Industrial Study
Bibliographic record
Abstract
Software Product Lines (SPLs) are families of related software products\ndeveloped from a common set of artifacts. Most existing analysis tools can be\napplied to a single product at a time, but not to an entire SPL. Some tools\nhave been redesigned/re-implemented to support the kind of variability\nexhibited in SPLs, but this usually takes a lot of effort, and is error-prone.\nDeclarative analyses written in languages like Datalog have been collectively\nlifted to SPLs in prior work, which makes the process of applying an existing\ndeclarative analysis to a product line more straightforward.\n In this paper, we take an existing declarative analysis (behaviour\nalteration) written in the Grok declarative language, port it to Datalog, and\napply it to a set of automotive software product lines from General Motors. We\ndiscuss the design of the analysis pipeline used in this process, present its\nscalability results, and provide a means to visualize the analysis results for\na subset of products filtered by feature expression. We also reflect on some of\nthe lessons learned throughout this project.\n
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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 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".