Predicting Higher Order Structural Feature Interactions in Variable Systems
Bibliographic record
Abstract
Robust and effective support for the detection and management of software features and their interactions is crucial for many development tasks but has proven to be an elusive goal despite extensive research on the subject. This is especially challenging for variable systems where multiple variants of a system and their features must be collectively considered. Here an important issue is the typically large number of feature interactions that can occur in variable systems. We propose a method that computes, from a set of known source code level interactions of n features, the relevant interactions involving n+1 features. Our method is based on the insight that, if a set of features interact, it is much more likely that these features also interact with additional features, as opposed to completely different features interacting. This key insight enables us to drastically prune the space of potential feature interactions to those that will have a true impact at source code level. This substantial space reduction can be leveraged by analysis techniques that are based on feature interactions (e.g Combinatorial Interaction Testing). Our observation is based on eight variable systems, implemented in Java and C, totaling over nine million LoC, with over seven thousand feature interactions.
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".