Process Models of SDLCs
Bibliographic record
Abstract
The software engineering discipline has developed the concept of software process to guide development teams towards a high-quality end product to be delivered on-time and within the planned budget. Consequently,several software-systems development life-cycles (PM-SDLCs) have been theoretically formulated and empirically tested over the years. In this chapter, a conceptual research methodology is used to review the state of the art on the main PM-SDLCs formulated for software-intensive systems, with the aim to answer the following research questions: (a) What are the main characteristics that describe the PM-SDLCs?, (b) What are the common and unique characteristics of such PM-SDLCs?, and (c) What are the main benefits and limitations of PM-SDLCs from a viewpoint of a conceptual analysis? This research is motivated by a gap in the literature on comprehensive studies that describe and compare the main PMSDLCs and organizes a view of the large variety of PM-SDLCs.
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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 | 0.000 | 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 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".