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 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.012 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".