Supporting Software Release Planning Decisions for Evolving Systems
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
Large-scale software systems constantly change during system evolution for feature enhancement. Most of the features originate from diverse stakeholders that require their needs to be met despite resource and risk constraints. In such large systems, the number of features requested during the different releases of the system typically exceeds the available resources. Release planning involves decision making about what new features or changes to implement during which release of the software. Existing release planning techniques are not targeted at evolving systems; in this case, knowledge about existing software product is core to making meaningful release decisions. In this paper, we describe ten key technical and nontechnical aspects impacting release planning. Based on these aspects, we evaluate seven existing release planning methods. We have also proposed a new release planning framework that considers the effect of existing system characteristics on release planning decisions. Initial realization of this framework focuses on historical defect data to characterize the health of system components. This proposed approach extends the existing solution method called EVOLVE* by (i) the proactive analysis of the risk involved in integrating new features into existing components of the system and (ii) identifying the importance of estimating the integration effort for each feature based on system characteristics. An illustrative example is also presented
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.
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 | 0.004 |
| 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 | 0.001 | 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