A semi-automated iterative process for detecting feature interactions
Bibliographic record
Abstract
Apresentação do artigo: "A semi-automated iterative process for detecting feature interactions" para trilha de Research do SBES 2020. Abstract: For configurable systems, features developed and tested separately may present a different behavior when combined in a system. Since software products might be composed of thousands of features, developers should guarantee that all valid combinations work properly. However, features can interact in undesired ways, resulting in failures. A feature interaction is an unpredictable behavior that cannot be easily deduced from the individual features involved. We proposed VarXplorer to inspect feature interactions as they are detected and incrementally classify them as benign or problematic. Our approach provides an iterative analysis of feature interactions allowing developers to focus on suspicious cases. In this paper, we present an experimental study to evaluate our iterative process of tests execution. We aim to understand how VarXplorer could be used for a faster and more objective feature interaction analysis. Our results show that VarXplorer may reduce up to 50% the amount of interactions a developer needs to check during the testing process.
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".