User Needs Analysis and requirements engineering: Theory and practice
Bibliographic record
Abstract
Several comprehensive User Centred Design methodologies have been published in the last decade, but while they all focus on users, they disagree on exactly what activities should take place during the User Needs Analysis, what the end products of a User Needs Analysis should cover, how User Needs Analysis findings should be presented, and how these should be documented and communicated. This paper highlights issues in different stages of the User Needs Analysis that appear to cause considerable confusion among researchers and practitioners. It is our hope that the User-Centred Design community may begin to address these issues systematically. A case study is presented reporting a User Needs Analysis methodology and process as well as the user interface design of an application supporting communication among first responders in a major disaster. It illustrates some of the differences between the User-Centred Design and the Requirements Engineering communities and shows how and where User-Centred Design and Requirements Engineering methodologies should be integrated, or at least aligned, to avoid some of the problems practitioners face during the User Needs Analysis.
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 | 0.067 | 0.082 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.004 | 0.035 |
| Scholarly communication | 0.015 | 0.019 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".