Proceedings of the 2nd International Workshop on Recommendation Systems for Software Engineering
Bibliographic record
Abstract
Recommendation systems for software engineering are tools that help developers and managers to better cope with the huge amount of information faced in today's software projects. They provide developers with information to guide them in a number of activities (e.g., software navigation, debugging, refactoring), or to alert them of potential issues (e.g., conflicting changes, failure-inducing changes, duplicated functionality). Similarly, managers get only to see the information that is relevant to make a certain decision (e.g., bug distribution when allocating resources). Recommendation systems can draw from a wide variety of input data, and benefit from different types of analyses. Although many recommendation systems have demonstrable usefulness and usability in software engineering, a number of questions remain to be discussed and investigated: What recommendations do developers and managers actually need? How can we evaluate recommendations? Are there fundamentally different kinds of recommenders? How can we integrate recommendations from different sources? How can we protect the privacy of developers? How can new recommendation systems leverage lessons from existing ones? In this workshop, we will study advances in recommendation systems, with a special focus on evaluation, integration, and usability.
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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 | 0.021 | 0.034 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.036 | 0.015 |
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".