Using task context to improve programmer productivity
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
When working on a large software system, a programmer typically spends an inordinate amount of time sifting through thousands of artifacts to find just the subset of information needed to complete an assigned task. All too often, before completing the task the programmer must switch to working on a different task. These task switches waste time as the programmer must repeatedly find and identify the information relevant to the task-at-hand. In this paper, we present a mechanism that captures, models, and persists the elements and relations relevant to a task. We show how our task context model reduces information overload and focuses a programmer's work by filtering and ranking the information presented by the development environment. A task context is created by monitoring a programmer's activity and extracting the structural relationships of program artifacts. Operations on task contexts integrate with development environment features, such as structure display, search, and change management. We have validated our approach with a longitudinal field study of Mylar, our implementation of task context for the Eclipse development environment. We report a statistically significant improvement in the productivity of 16 industry programmers who voluntarily used Mylar for their daily work.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 | 0.001 |
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 it