Planning for the next software release using adaptive network-based fuzzy inference system
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.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".