Observing and measuring cognitive support: steps toward systematic tool evaluation and engineering
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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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 | 0.139 | 0.276 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.013 | 0.006 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.011 | 0.016 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".