Variability-aware Neo4j for Analyzing a Graphical Model of a Software Product Line
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
Comprehensive analysis of a software product line (SPL) is expensive because the number of products to be analyzed is exponential in the number of the SPL’s features. To compensate, we analyze a model of the SPL rather than the source code, thereby reducing the size of the artifact under analysis. In this paper, we facilitate SPL analysis by lifting the Neo4j query engine to apply to an SPL model, so that a Neo4j query returns variability-aware results that cover all the SPL’s products. We used the lifted Neo4j to analyze five nontrivial SPLs (with respect to dataflows, control-flows, component interactions, etc.) and found that the overhead for returning variability-aware results for the full SPL, versus the results for just one product, ranges from 1.88% to 456%. In comparison to related work V-Soufflé (a lifted Datalog engine), lifted Neo4j is able to report complete path results whereas V-Soufflé reports only endpoints of paths. When both analyzers report the same results (e.g., endpoints of paths), lifted Neo4j is usually more efficient.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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