Ensemble Balanced Nested Dichotomy Fuzzy Models for Software Requirement Risk Prediction
Bibliographic record
Abstract
Modern software systems are becoming more intricate, making identification of risks in the software requirement phase— a fundamental aspect of the software development life cycle (SDLC)—complex. Inadequate risk assessment may result in the malfunction of a software system, either in the development or production phase. Therefore, risk prediction plays a crucial role in software requirements, serving as the first step in any software project. Hence, developing adaptive predictive models that can offer consistent and explainable insights for handling risk prediction is imperative. This study proposes novel ensemble class balanced nested dichotomy (EBND) fuzzy induction models for risk prediction in software requirement. Specifically, the proposed EBND models employ a hierarchical structure consisting of binary trees featuring distinct nested dichotomies that are generated randomly for each tree. Thereafter, we use an ensemble principle to refine rules generated from the resulting binary tree. The predictive efficacy of the suggested EBND models is further extended by introducing a data sampling method into their prediction process. The inclusion of the data sampling method acts to mitigate the underlying disparity in the class labels that may affect its prediction processes. The efficacy of the EBND models is then evaluated and compared to current solutions using the open-source software risk dataset. The observed findings revealed that the EBND models demonstrated superior predictive capabilities when compared to the conventional models and state-of-the-art methodologies. Specifically, the EBND models achieved an average accuracy threshold value of 98%, as well as high values for the f-measure metric.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".