Scope Management of Non-Functional Requirements
Bibliographic record
Abstract
In order to meet commitments in software projects, a realistic assessment must be made of project scope. Such an assessment relies on the availability of knowledge on the user-defined project requirements and their effort estimates and priorities, as well as their risk. This knowledge enables analysts, managers and software engineers to identify the most significant requirements from the list of requirements initially defined by the user. In practice, this scope assessment is applied to the functional requirements (FRs) provided by users who are unaware of, or ignore, the non-functional requirements (NFRs). This paper presents ongoing research which aims at managing NFRs during the software development process. Establishing the relative priority of each NFR, and obtaining a rough estimate of the effort and risk associated with it, is integral to the software development process and to resource management. Our work extends the taxonomy of the NFR framework by integrating the concept of the "hardgoal". A functional size measure of NFRs is applied to facilitate the effort estimation process. The functional size measurement method we have chosen is COSMIC-FFP, which is theoretically sound and the de facto standard in the software industry.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".