Enhancing Agile Project Management for Remote Teams: A Graph Theory-Based Approach
Bibliographic record
Abstract
The rapidly evolving video game industry faces the challenge of managing increasingly complex development projects, often involving interdisciplinary teams.These teams frequently operate remotely, at least partially, and typically rely on the Scrum project management framework, which is an adaptive approach within the Agile methodology.However, Scrum was originally designed for small, co-located teams with direct, face-to-face communication, making it less effective in the context of virtual teams.The transition to remote work has highlighted several challenges, particularly regarding communication and trust among team members, which are not adequately addressed by the traditional Scrum tools.To address these issues, this paper introduces a novel software tool that integrates graph theory to enhance the Scrum project management process.Recognizing that chat platforms, such as Microsoft Teams, are the primary mode of communication in remote work environments, our tool leverages data from these platforms.It performs both quantitative and qualitative analyses of various graph-based metrics to assess the health of team communication and provide actionable feedback to managers.This tool, developed in Python, has been tested using synthetic communication scenarios generated by Chat GPT.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".