Knowledge-Oriented Models Based on Developer-Artifact and Developer-Developer Interactions
Bibliographic record
Abstract
INTRODUCTION: Software development is organized around developers working collaboratively promoting two types of interactions for knowledge sharing. Developer-Artifact interactions indicate developers define or access pieces of information within artifacts. Developer-Developer interactions indicate the exchange of information among developers using a collaboration platform to clarify an issue, promote an idea, or expose any thoughtful comment. PROBLEM: The number of such interactions grows over time and makes it difficult to capture and assess the evolution of the developers' knowledge about specific software project artifacts and tasks. Further, this knowledge decreases over time due to the natural limitations of human cognition that restrict our capabilities to cope with information overload. Besides, who has more knowledge about specific project elements are important to promote collaboration. AIMS: The Ka, Ks, Kc, and Kpmodels capture the evolution of the developers' knowledge about software project elements such as artifacts, tasks, similar tasks, and the whole software project. These models represent not only the knowledge developers have about these elements but also capture how this knowledge decreases over time based on forgetting and relearning functions. EVALUATION: An experimental study analyzed some developers' interactions on artifacts for the purpose of predicting the evolution of developers' knowledge in six software projects. The results show that the developers' rankings by performed tasks and by our models have 72% or more of similarity. CONCLUSION: Our models can capture and assess the evolution of the developers' knowledge and help to identify which developers have more knowledge about specific elements of software projects.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".