Prioritizing tasks in software development: A systematic literature review
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
Task prioritization is one of the most researched areas in software development. Given the huge number of papers written on the topic, it might be challenging for IT practitioners-software developers, and IT project managers-to find the most appropriate tools or methods developed to date to deal with this important issue. The main goal of this work is therefore to review the current state of research and practice on task prioritization in the Software Engineering domain and to individuate the most effective ranking tools and techniques used in the industry. For this purpose, we conducted a systematic literature review guided and inspired by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses, otherwise known as the PRISMA statement. Based on our analysis, we can make a number of important observations for the field. Firstly, we found that most of the task prioritization approaches developed to date involve a specific type of prioritization strategy-bug prioritization. Secondly, the most recent works we review investigate task prioritization in terms of "pull request prioritization" and "issue prioritization," (and we speculate that the number of such works will significantly increase due to the explosion of version control and issue management software systems). Thirdly, we remark that the most frequently used metrics for measuring the quality of a prioritization model are f-score, precision, recall, and accuracy.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| 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