Exploring the Landscape of Multicriteria Decision Making in Software Project Management: Trends, Challenges, and Future Directions
Bibliographic record
Abstract
INTRODUCTION: This critical review investigates the utilization trends of Multicriteria Decision Making (MCDM) in software project management, emphasizing its applications, implementation challenges, and emerging trends.OBJECTIVES: The study explores recent literature published between 2019 and 2024, utilizing a systematic methodology to analyze the effectiveness and limitations of MCDM techniques in software project planning, selection, and execution.METHODS: A Boolean search strategy on Scopus was employed to identify relevant literature. The systematic methodology involved analyzing the identified literature to discern patterns, gaps, and recommendations for integrating MCDM methodologies within software engineering projects.RESULTS: The review identifies key patterns, challenges, and emerging trends in adopting MCDM techniques in software project management, providing insights and recommendations for future research and practice.CONCLUSION: This critical review offers valuable insights into the landscape of MCDM utilization in software project management, highlighting areas for improvement and future exploration.
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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 | 0.040 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.012 | 0.018 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| 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".