Acquiring Testable NFRs Utilizing Goal Models Enhancing Application Requirements Analysis in Goal-Driven Software Product Lines
Bibliographic record
Abstract
Software product line engineering involves reusing development assets to create a family of software systems with common features and few specific differences. Testing is crucial for evaluating software quality, encompassing both functional and non-functional requirements. However, non-functional requirements (NFRs) are often neglected in software product lines, with a primary focus on functional requirements during system configuration. This paper addresses the significance of early testing and the challenges of testing non-functional properties in software product lines. To ensure effective testing of non-functional aspects, clear and testable specifications for non-functional requirements are essential. Current practices often leave non-functional requirements unaddressed until system testing, lacking proper traceability and management. Additionally, systematic approaches are lacking to support non-functional requirements from the early stages of development, hindering their integration into product line development. In this research, we propose an approach that utilizes goal models to enhance application requirements analysis in goal-driven software product lines. By incorporating goal models, our approach enables the acquisition of testable non-functional requirements during the early stages of development. Our approach aims to improve the effectiveness and efficiency of testing non-functional properties and align them with the specified goals.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".