Agile Methods and User-Centered Design: How These Two Methodologies are Being Successfully Integrated in Industry
Bibliographic record
Abstract
A core principle of agile development is to satisfy the customer by providing valuable software on an early and continuous basis. For a software application to be valuable it should have a user interface that is usable. Recently there has been some evidence that suggests using agile methods alone does not ensure that an applications UI is usable. As a result, there is currently interest in combining Agile methods with user-centered design (UCD) practices. To support existing empirical evidence that these methodologies co-exist effectively we have conducted a study with participants that have previously combined these two methodologies. Our findings, combined with existing work show that the existing model used for agile UCD integration can be broadened into a more common model. In this paper we describe three different approaches taken by our participants to achieve this integration. We term these approaches the generalist, specialist, and the hybrid approach.
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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 | 0.044 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.024 |
| Scholarly communication | 0.018 | 0.014 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.005 | 0.007 |
| 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".