Improving the Identification of Hedonic Quality in User Requirements — A Controlled Experiment
Bibliographic record
Abstract
Context and Motivation Systematically engineering a good user experience (UX) into a computer-based system under development demands that the user requirements of the system reflect all needs, including emotional, of all stakeholders. User requirements address two different types of qualities: pragmatic qualities (PQs), that address system functionality and usability, and hedonic qualities (HQs) that address the stakeholder's psychological well-being. Studies show that users tend to describe such satisfying UXes mainly with PQs, and that some users seem to believe that they are describing a HQ when they are actually describing a PQ. Question/Problem The problem is to see if classification of any user requirement as PQ-related or HQ-related is difficult, and if so, why. Principal Ideas/Results We conducted a controlled experiment in which twelve requirements-engineering and UX professionals, hereinafter called "classifiers" classified each of 105 user requirements as PQ-related or HQ-related. The experiment shows that neither (1) a classifier's involvement in the project from which the requirements came nor (2) the classifier's use of a detailed model of the qualities in addition to the standard definitions of "PQ" and "HQ" has a positive effect on the consistency of the classifier's classification with that of others. Contribution The experiment revealed that classification of user requirements is a lot harder than initially assumed.
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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 | 0.020 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".