Toward Optimal Psychological Functioning in AI-Driven Software Engineering Tasks: The Software Evaluation for Well-Being and Optimal Psychological Functioning in a Context-Aware Environment Assessment Framework
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
In the field of software engineering, there has been a shift towards utilizing various artificial intelligence techniques to address challenges and create innovative tools. These solutions are aimed at enhancing efficiency, automating tasks, and providing valuable support to developers. While the technical aspects are crucial, the well-being and psychology of the individuals performing these tasks are often overlooked. This paper argues that a holistic approach is essential, one that considers the technical, psychological, and social aspects of software engineering tasks. To address this gap, we introduce SEWELL-CARE, a conceptual framework designed to assess AI-driven software engineering tasks from multiple perspectives, designed with the goal of customizing the tools to improve the efficiency, well-being, and psychological functioning of developers. By emphasizing both technical and human dimensions, our framework provides a nuanced evaluation that goes beyond traditional technical metrics.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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