Planning for the next software release using adaptive network-based fuzzy inference system
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The release planning process concerns with assigning requirements to the different future releases of the software. This paper considers three factors that govern the release planning process: stakeholders' satisfaction, risk, and availability of resources. All of these factors depend on human know ledge, which is always incomplete, imprecise, and approximated. This classifies release planning as an under-uncertainty decision-making problem. This paper proposes a prioritization approach for generating a release plan for the next release of the software. The proposed approach employs a fuzzy inference system engine in order to tackle the uncertainty in the release planning process. The artifacts of the fuzzy inference (FIS) process (the membership functions and the IF-rules) are constructed using adaptive network-based fuzzy inference system (ANFIS). ANFIS helps to reinforce the human knowledge with the knowledge obtained from the historical data. Experiments show that the outputs of the proposed framework are affected by the reliability, accuracy, and the orientation of the historical data used to train the ANFIS module. For example, when training the ANFIS module using data that concentrates on the factor of stakeholders' satisfaction, the proposed framework has shown very good results from the perspective of this factor.
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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 | 0.001 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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 it