Leveraging belief uncertainty for informed decision making in software product line evolution
Bibliographic record
Abstract
Software Product Lines (SPL) are not static software artifacts, but they evolve over time. The planning, realization, and release of a SPL requires many high-level decisions involving many different stakeholders with different expertise. Taking their opinions into account to make the right decisions is not trivial. Currently there are no mechanisms to assist stakeholders in the decision making process in an informed manner. In this paper, we propose the use of belief uncertainty in conjunction with feature models to assist in the evolution of SPLs by explicitly quantifying opinions and their associated uncertainty. We present a novel approach in which subjective logic is used to represent the opinions of stakeholders in three evolution scenarios, namely feature model evolution, next release problem and variability reduction. We apply our approach to the evolution of the Xiaomi MiBand SmartWatch SPL over the time period from July 2014 to October 2023. We present an implementation of our approach and evaluate its scalability. • Novel approach for decision making during SPL evolution. • Applicable to different evolution scenarios: feature planning, next release problem and variability reduction. • Use of subjective logic to quantify stakeholder beliefs (opinions). • Illustration on the evolution of a real-world SPL – the Xiaomi MiBand SPL – from July 2014 to October 2023. • Proof of concept implementation in Python.
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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 | 0.009 | 0.039 |
| 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 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".