A structural taxonomy for lifted software product line analyses
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
A software product line (SPL) is a structured collection of distinct software products developed from a common set of artifacts. SPLs can encompass millions of products, so analyzing each product in a brute-force manner is infeasible. To analyze SPLs directly, analyses must be lifted , i.e., redefined to accommodate the semantics of SPLs. Over the past two decades, many kinds of analyses have been lifted from products to SPLs. Looking at the landscape of lifted analyses, we observe various techniques for lifting which vary across numerous dimensions. To help engineers and research navigate this landscape, we propose a classification scheme for lifted analyses based on a set of features lifted analyses can exhibit. We then conduct a systematic literature review (SLR) analyzing the landscape of lifted analyses produced over the last 20 years. We analyze 140 research papers which discuss the design and implementation of lifted analyses. We provide quantitative analysis of the types of analyses which have been lifted, and apply our taxonomy to clarify how lifting was accomplished. We discuss examples of how each of the lifting methods have been applied, and identify gaps in the research literature which may provide directions for future work. • We propose dimensions by which lifted analyses can be classified into implementation patterns. • We propose structural dimensions by which lifted analyses can be classified. • Using a literature review, we identify patterns in how lifting is done in practice.
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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 | 0.001 | 0.002 |
| 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