The use of search‐based optimization techniques to schedule and staff software projects: an approach and an empirical study
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Bibliographic record
Abstract
Abstract Allocating resources to a software project and assigning tasks to teams constitute crucial activities that affect project cost and completion time. Finding a solution for such a problem is NP‐hard; this requires managers to be supported by proper tools for performing such an allocation. This paper shows how search‐based optimization techniques can be combined with a queuing simulation model to address these problems. The obtained staff and task allocations aim to minimize the completion time and reduce schedule fragmentation. The proposed approach allows project managers to run multiple simulations, compare results and consider trade‐offs between increasing the staffing level and anticipating the project completion date and between reducing the fragmentation and accepting project delays. The paper presents results from the application of the proposed search‐based project planning approach to data obtained from two large‐scale commercial software maintenance projects. Copyright © 2011 John Wiley & Sons, Ltd.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| 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