Towards Efficient Tracing in Software Product Lines: Research Methodology
Bibliographic record
Abstract
Software Product Lines represent a solution for massive development with minimum costs, while assuring product high quality and interesting time to market. In fact, Software Product Lines systems are used for massive productions, and are based on systematic reuse of commun components, while offering the ability to add specific development, in order to satisfy particular users or market needs. However, to maintain such complex and large-scale systems, it is mandatory to adopt a suitable tracing policy that satisfies the system constraints, especially cost and complexity. Unfortunately, tracing is rearly applied in Software Product Lines as it presents several constraints, especially its cost. Through our research work, we tried to come up with elements that would help break this prejudice. Therefore, we worked on a cost and Return on Investment estimation model that helps identify the optimal conditions (phase and policy) for implementing a tracing solution. As a result of our work, we found that implementing specific trace links, in a targeted approach that meets business goals, and starting from the Domain Engineering phase, costs less and presents the most interesting Return on Investment. To conduct this study and reach those findings, we followed the Design Science Research Methodology. In this article, we detail the steps of our research according to this methodology’s phases.
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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 | 0.014 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".