The Agile Transition in Software Development Companies: The Most Common Barriers and How to Overcome Them
Bibliographic record
Abstract
The purpose of this paper is to investigate the most common barriers facing the greater adoption of Agile approaches to project management, and ways to overcome these barriers during an Agile transition. First, based on a literature review, this paper describes the Agile approaches and practices in general. The review also covers the previous work around the adoption of Agile, which provides considerable information about the challenges of doing so. This includes some prerequisites, key decisions, transitional frameworks, and recommendations to overcome organisational, cultural, and structural barriers. Next, this paper reports on a recently conducted Agile project management survey. Using this method, this research project gathered information about the important issues that software development companies have to overcome in order to be successful in an Agile transition. The survey was given to Scrum masters, project managers, chief executive officers, and IT professionals, who have participated in companies that have migrated from a traditional methodology to an Agile methodology. Several barriers were highlighted: general organisational resistance to change, lack of user/customer availability, pre-existing rigid framework, not enough personnel with Agile experience, concerns about loss of management control, concerns about lack of upfront planning, insufficient management support, concerns about the ability to scale Agile, need for development team support, and the perceived time and cost to make the transition. Finally, the paper offers concise recommendations to overcome each of the barriers as well as ideas for future research.
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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 | 0.016 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| 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".