A Comprehensive Framework for Testing Goal‐Oriented NFPs in Software Product Lines
Bibliographic record
Abstract
ABSTRACT In the realm of software product line engineering (SPLE), ensuring the quality of end products is paramount for market success. SPLE promotes systematic software development through reuse by focusing on commonalities and variabilities within a domain to efficiently produce a family of related systems. The quality of a software system depends on its functional properties (FPs)—the functionalities it provides—and its non‐functional properties (NFPs)—the quality attributes it possesses, such as security and performance. NFPs are particularly critical because they directly impact user satisfaction, determine project success, and significantly influence market acceptance. However, in SPLE, despite their recognized importance, NFPs often receive less attention compared to FPs, leading to potential quality risks and increased costs. This paper presents a framework for testing goal‐oriented NFPs in software product lines, addressing this gap. By integrating goal models, the framework supports the systematic capture and validation of NFPs from early development stages. The framework's applicability is illustrated through research‐based case studies in an online bookstore product line, demonstrating its use for systematic NFPs testing at both the domain and application levels. A comparative analysis with an existing technique highlights the framework's unique contributions in addressing NFPs testing within software product lines. Additionally, a preliminary experiment using two widely recognized product line domain examples evaluated the core testing process supported by the framework during the domain engineering phase, focusing on effectiveness, performance efficiency, and time consistency in structured research settings.
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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 | 0.017 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.008 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".