Issue Prioritization in Agile Development Through the Lens of Constraint Solving
Bibliographic record
Abstract
Issue prioritization in agile software development remains a challenging task that is often addressed through classification rather than explicit ordering. This paper explores the use of an efficient constraint solver, CP-SAT, from Google to automatically generate explicit issue orderings in agile development sprints. Using historical data from real-world agile projects managed in Jira, we evaluate how well different constraint configurations approximate a gold-standard resolution order. Additionally, we simulate user elicitation to resolve ambiguities in cases where the solver produces multiple orderings, including scenarios where user input may deviate from the gold standard due to user errors. By varying both the quantity and accuracy of user elicitation, we evaluate its impact on the quality of the generated prioritization of the issues. This analysis aims to identify the constraint configurations that best replicate the orderings of real-world issue resolutions. Our findings suggest the system accurately captures improvements in the solution space with increased elicitation and remains robust to user errors. All three constraints: priority class, issue type, and creation date potentially impact prioritization.
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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 | 0.009 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".