Comparison between traditional plan-based and agile software processes according to team size & project domain (A systematic literature review)
Bibliographic record
Abstract
Background: Agile Software development is becoming the most preferred approach for the process of software development. Since the traditional plan-based methods are rigorous and not flexible with changing requirements, some projects are postponed, go over budget, and are sometimes canceled or started from scratch. Questions remain on whether the traditional plan-based approaches will be replaced by agile. For effective, flexible and high-quality projects, organizations are shifting to flexible methods, where they can change the requirements at any stage of the development process. The purpose of this paper is to compare the plan-based and agile software development processes. The paper will discuss the art of deciding which methodology should be used with regard to the team size and the project domain. In the paper a systematic literature review covers 26 papers between 2000 and 2016. The papers are selected with reference to the size of the team and the domain of the project. The result of the paper illustrate that each methodology has a specific area which it best fits in. An organization should consider all factors and choose the methodology according to the situation. Finally, Agile best fits with small team sizes, for exploratory, and software & web-based projects. Traditional methods best fit large team sizes, for predictable, and reusable artifacts projects. However, they can co-exist.
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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 | 0.031 | 0.110 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.031 | 0.025 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".