Cognitive and Hierarchical Fuzzy Inference System for Generating Next Release Planning in SaaS Applications
Bibliographic record
Abstract
<p>The next release planning is considered as a cognitive decision-making problem where many stakeholders provide their judgments and opinions about the set of features that shall be included in the next release of the software. In multi-tenant Software as a Service (SaaS) applications, planning for the next release is a significant process that plays important roles in the success of SaaS applications. SaaS providers shall fulfill the evolving needs and requirements of their tenants by continuously delivering new releases. The first step in a release development lifecycle is the release planning process. This paper proposes a novel approach for the next release planning for multi-tenant SaaS applications. This approach is a prioritization approach that employs a hierarchical fuzzy inference system (HFIS) module to deal with the uncertainty associated with human judgments. The main objectives of the proposed approach are maximizing the degree of overall tenants’ satisfaction, maximizing the degree of commonality, and minimizing the potential risk, while considering contractual, effort, and dependencies constraints. The performance of the proposed approach is validated against a one from the literature and shows better results from the perspective of overall tenants’ satisfaction and adherence to the risk</p>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".