Bi-objective release planning for evolving software systems
Why this work is in the frame
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Bibliographic record
Abstract
The release planning (RP) problem can be investigated from two dimensions -- what to release and when to release. We investigate the "what" to release decision in terms of which new features or change requests should be assigned and implemented in which releases of a software system. RP for evolving systems is challenging, because the new features might require changes to the existing system. A major drawback of existing RP methods is that, they do not consider the existing systems in making RP decisions. In this paper, we present a technique to detect coupling between features based on relatedness of the components that would implement the features. The components implementing the features are derived from change impact analysis. We integrate the results from feature coupling into a RP strategy that encourages the assignment of highly coupled features in the same release. This helps to avoid haphazard implementation of related features. We present a decision support approach that formulates the RP problem as a bi-objective optimization problem. Our Bi-Objective Release Planning for Evolving Systems (BORPES) is aimed at optimizing the value of release plans from both the business perspectives and the implementation perspectives. This paper presents BORPES in detail and reports on a proof-of-concept case study that investigates the applicability of the proposed approach. The bi-objective optimization offers a set of Pareto-optimal solutions.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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