An effective framework to improve the managerial activities in global software development
Bibliographic record
Abstract
Abstract Global Software Development (GSD) is a contemporary approach to software development that offers numerous advantages, including enhanced cost-effectiveness and timely delivery. It enables access to a vast pool of skilled developers and facilitates the exchange of best practices and innovative ideas within the software industry. However, effective project management plays a vital role in ensuring successful product development. Organizations that achieve project success consistently adhere to well-defined project management methodologies, resulting in desired outcomes within predefined time frames and allocated resources. The success rate of software projects significantly increases with diligent software management efforts. Nevertheless, the distributed nature of GSD presents significant challenges related to collaboration, information dissemination, and process control, which ultimately impede effective development and compromise software quality. In this study, we identify various challenges associated with the GSD process and propose strategies to overcome obstacles to effective project management. Additionally, we introduce a comprehensive framework designed to enhance managerial activities in GSD.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".