Automated planning for feature model configuration based on functional and non-functional requirements
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
Feature modeling is one of the main techniques used in Software Product Line Engineering to manage the variability within the products of a family. Concrete products of the family can be generated through a configuration process. The configuration process selects and/or removes features from the feature model according to the stakeholders' requirements. Selecting the right set of features for one product from amongst all of the available features in the feature model is a complex task because: 1) the multiplicity of stakeholders' functional requirements; 2) the positive or negative impact of features on non-functional properties; and 3) the stakeholders' preferences w.r.t. the desirable non-functional properties of the final product. Many configurations techniques have already been proposed to facilitate automated product derivation. However, most of the current proposals are not designed to consider stakeholders' preferences and constraints especially with regard to non-functional properties. We address the software product line configuration problem and propose a framework, which employs an artificial intelligence planning technique to automatically select suitable features that satisfy both the stakeholders' functional and non-functional preferences and constraints. We also provide tooling support to facilitate the use of our framework. Our experiments show that despite the complexity involved with the simultaneous consideration of both functional and non-functional properties our configuration technique is scalable.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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