A Multistep Approach for Managing the Risks of Software Requirements Volatility
Bibliographic record
Abstract
Software is changed continuously in order to respond to different users and business needs. Requirements are changed dynamically to improve software usability and increase its value, but requirement volatility sometimes cause failures for many projects because of inadequate understanding of the changing causes and the consequences of these changes. This research highlights the importance of managing requirement changes, classify them, and control the impact risks of requirement volatility on software project. The proposed model is designed based on software requirements risks factors and how to reduce their impacts. Generally, requirements changing is considered as a difficult, costly and time-consumed task, and consequently it is too important to study the inter-relationships between the changes and their impacts on the other phases of software system. The good understanding of the changing causes and their consequences can improve and support requirements management process and also lead successfully to the predicted goals of changes. The high quality of the requirements influences the success of a software project during software development and maintenance processes.
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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 | 0.014 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| 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".