Goal-Oriented Test Case Selection and Prioritization for Product Line Feature Models
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
The software product line engineering paradigm is amongst the widely used means for capturing and handling the commonalities and variabilities of the many applications of a target domain. The large number of possible products and complex interactions between software product line features makes the effective testing of them a challenge. To conquer the time and space complexity involved with testing a product line, an intuitive approach is the reduction of the test space. In this paper, we propose an approach to reduce the product line test space. We introduce a goal-oriented approach for the selection of the most desirable features from the product line. Such an approach allows us to identify the features that are more important and need to be tested more comprehensively from the perspective of the domain stakeholders. The more important features and the configurations that contain them will be given priority over the less important configurations, hence providing a hybrid test case reduction and prioritization strategy for testing software product lines.
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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 | 0.001 |
| 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