Formal and quantitative approach to non-functional requirements modeling and assessment in software engineering
Bibliographic record
Abstract
In the software market place, in which functionally equivalent products compete for the same customer, Non Functional Requirements (NFRs) become more important in distinguishing between the competing products. However, in practice, NFRs receive little attention relative to Functional Requirements (FRs). This is mainly because of the nature of these requirements which poses a challenge when taking the choice of treating them earlier in the software development. NFRs are subjective, relative and they become scattered among multiple modules when they are mapped from the requirements domain to the solution space. Furthermore, NFRs can often interact, in the sense that attempts to achieve one NFR can help or hinder the achievement of other NFRs at particular software functionality. Such an interaction creates an extensive network of interdependencies and tradeoffs among NFRs which is not easy to trace or estimate. This thesis contributes towards achieving the goal of managing the attainable scope and the changes of NFRs. The thesis proposes and empirically evaluates a formal and quantitative approach to modeling and assessing NFRs. Central to such an approach is the implementation of the proposed NFRs Ontology for capturing and structuring the knowledge on the software requirements (FRs and NFRs), their refinements, and their interdependencies. In this thesis, we also propose a change management mechanism for tracing the impact of NFRs on the other constructs in the ontology and vice-versa. We provide a traceability mechanism using Datalog expressions to implement queries on the relational model-based representation for the ontology. An alternative implementation view using XML and XQuery is provided as well. In addition, we propose a novel approach for the early requirements-based effort estimation, based on NFRs Ontology. The effort estimation approach complementarily uses one standard functional size measurement model, namely COSMIC, and a linear regression technique
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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 | 0.025 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| 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".