A Model for Characterizing Web Engineering
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The Internet, particularly the Web, has opened new vistas for many sectors of society, and over the last decade it has played an increasingly integral role in our daily activities of communication, information, and entertainment. This evidently has had an impact on how Web applications are perceived, developed, and managed. The need to manage the size, complexity, and growth of Web applications has led to the discipline of Web engineering (Ginige & Murugesan, 2001). It is known (Kruchten, 2004) that conventional engineering practices cannot be simply mapped to software engineering without the engineer first understanding the nature of the software, and we contend the same applies to Web engineering. This article proposes a systematic approach to identify and elaborate the characteristics that make Web engineering a unique discipline, and considers the implications of these characteristics. The rest of the article is organized as follows. We first outline the background and related work necessary for the discussion that follows, and state our position in that regard. This is followed by a model to uniquely posit the nature of Web applications based on the dimensions of project, people, process, product, and resources. 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.
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 | 0.000 | 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 | 0.000 |
| 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 it