Bibliographic record
Abstract
The requirements engineering (RE) process and resultant requirements usually inform and interact with downstream (e.g., design and testing) and side-stream (e.g., project management, quality management) processes in various ways. Each of these processes involves numerous internal stakeholders (e.g., managers, developers, architects, etc.) who, in turn, have different concerns with regard to the impact of requirements on their respective processes. In other words, the various stakeholders need different types of requirements information and measurements in order for them to manage, control, and track their respective process activities (e.g., design traceability information for architects, requirements progress for project managers, etc.). The burden of providing this information usually falls within the realm of the requirements management process. However, due to the lack of identified metrics and analytical methods, the process of providing the various stakeholders with the information that addresses their various concerns becomes cumbersome. This is further complicated by large project sizes, numerous stakeholders, time pressure, large numbers of requirements, other software artifacts, and others. This proposal aims to address this problem by proposing to provide stakeholders with concern-driven requirements analytics that will address their various concerns. We intend to achieve this through identifying metrics and analytical methods that can be readily used in the requirements management process. We further propose to provide the stakeholder with a dashboard that allows them to choose from the various requirements analytics options along with visualization techniques that would best visualize the data and address their concerns.
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 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".