Bibliographic record
Abstract
The engineering environment of Web Applications is in a constant state of technological and social flux. These applications face challenges posed by new implementation languages, variations in user agents, demands for new services, and user classes from different cultural backgrounds, age groups, and capabilities. We require a methodical approach towards the development life cycle and maintenance of Web Applications that can adequately respond to this constantly changing environment. In this article, we propose the use of an agile methodology (Highsmith, 2002), namely Extreme Programming (XP) (Beck & Andres, 2005), for a systematic development of Web Applications. In general, agile methodologies have show to be cost-effective for projects with certain types of uncertainties (Liu, Kong, & Chen, 2006) and, according to surveys (Khan & Balbo, 2005), been successfully applied to Web Applications. The organization of the article is as follows. We first outline the background necessary for the discussion that pursues and state our position. This is followed by a discussion of the applicability and feasibility of XP practices as they pertain to Web Applications. Then the shortcomings of XP towards Web Applications are highlighted, and suggestions for improvement are presented. Next, challenges and directions for future research are outlined. Finally, concluding remarks are given.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.019 | 0.008 |
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".