Observing and measuring cognitive support: steps toward systematic tool evaluation and engineering
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
A key desideratum for many software comprehension tools is to reduce the mental burdens of software engineers. That is, the tools should support cognition. This key benefit is difficult to directly observe and measure, so evaluating such tools has been problematic. This paper describes an investigation into the application of distributed cognition theories to analyzing and observing cognitive support. Theories of cognitive support are used to generate an analysis of potential cognitive benefits provided by the compilation-error tracking facilities of a commercial software development environment. This analysis is used to generate a scheme for coding user observations such that cognitive support related activity can be tracked. Experiences in applying the technique on data from a field study are reported. The study also serves to provide a glimpse into the ways that programmers and tools cooperate. Implications are drawn for future practices of tool evaluation and engineering.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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