A systematic mapping study of information visualization for software product line engineering
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
Abstract Software product lines (SPLs) are families of related systems whose members are distinguished by the set of features they provide. Over 2 decades of research and practice can attest to the substantial benefits of applying SPL practices such as better customization, improved software reuse, and faster time to market. Software product line engineering (SPLE) refers to the paradigm of developing SPLs. Typical SPLE efforts involve a large number of features that are combined to form also large numbers of products, implemented using multiple and different types of software artifacts. Because of the sheer amount of information and its complexity, visualization techniques have been used for different SPLE activities. In this paper, we present an extended systematic mapping study on this subject. Our research questions aim to gather information regarding the techniques that have been applied, at what SPLE activities, how they were implemented, the publication fora used, the methods of empirical evaluation, and the provenance of the evaluation examples. Our driving goal is to identify common trends, gaps, and opportunities for further research and application.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 |
| 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 | 0.003 |
| Open science | 0.001 | 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