Agile software development practices: evolution, principles, and criticisms
Bibliographic record
Abstract
Purpose Agile software development is an emerging approach in software engineering, initially proposed and promoted by a group of 17 software professionals who practice a set of “lightweight” methods, and share a common set of values of software development. They consolidated their thoughts, and defined these methods as “agile”. The approaches are based on experiences and best practices from the past by the above‐mentioned group of 17 software professionals. The purpose of this article is to outline the history and evolution of agile software development practices, their principles, and the criticisms as reported by the software development community. Design/methodology/approach A comprehensive literature review was undertaken to do this research. Findings Based on the literature review, this paper provides a comprehensive document that helps the practitioners working in the area of the agile software development. Originality/value This article will provide comprehensive material for the researchers in the area of agile software development. It will also be very useful for the practitioners practicing software development in the area of agile software development.
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 | 0.019 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".