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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".