Software product-line evaluation in the large
Bibliographic record
Abstract
Abstract Software product-line engineering is arguably one of the most successful methods for establishing large portfolios of software variants in an application domain. However, despite the benefits, establishing a product line requires substantial upfront investments into a software platform with a proper product-line architecture, into new software-engineering processes (domain engineering and application engineering), into business strategies with commercially successful product-line visions and financial planning, as well as into re-organization of development teams. Moreover, establishing a full-fledged product line is not always possible or desired, and thus organizations often adopt product-line engineering only to an extent that deemed necessary or was possible. However, understanding the current state of adoption, namely, the maturity or performance of product-line engineering in an organization, is challenging, while being crucial to steer investments. To this end, several measurement methods have been proposed in the literature, with the most prominent one being the Family Evaluation Framework (FEF), introduced almost two decades ago. Unfortunately, applying it is not straightforward, and the benefits of using it have not been assessed so far. We present an experience report of applying the FEF to nine medium- to large-scale product lines in the avionics domain. We discuss how we tailored and executed the FEF, together with the relevant adaptations and extensions we needed to perform. Specifically, we elicited the data for the FEF assessment with 27 interviews over a period of 11 months. We discuss experiences and assess the benefits of using the FEF, aiming at helping other organizations assessing their practices for engineering their portfolios of software variants.
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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 | 0.050 | 0.119 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".