Project-Based Software Risk Management Approaches
Bibliographic record
Abstract
The last few decades—especially the end of 20th century and the beginning of 21st century—have shown an increase in the interest in automation of different activities. Automation is dependent in its core on sound functional software. The complexity of software development has increased significantly over the years. Articles showing the failure of projects in the software industry are not surprising. Standish Group (1994) reports show that about 53% of projects get completed, but they do not meet the cost and schedule requirements, and about 31% are canceled before the completion of the projects. These failure reports are significantly alarming. With the tremendous growth in the complexity of software development in the last 10 to 15 years, the management of risks in software engineering activities is becoming an important and nontrivial issue from three perspectives: project, process, and product. Therefore, researchers and practitioners are continually trying to find effective risk management approaches. This article should help the academicians, researchers, and practitioners interested in the area of risk management in software engineering to gain an overall understanding of the area.
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 | 0.015 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".