Detecting Developers’ Task Switches and Types
Why this work is in the frame
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Bibliographic record
Abstract
Developers work on a broad variety of tasks during their workdays and constantly switch between them. While these task switches can be beneficial, they can also incur a high cognitive burden on developers, since they have to continuously remember and rebuild the task context–the artifacts and applications relevant to the task. Researchers have therefore proposed to capture task context more explicitly and use it to provide better task support, such as task switch reduction or task resumption support. Yet, these approaches generally require the developer to <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">manually</i> identify task switches. Automatic approaches for predicting task switches have so far been limited in their accuracy, scope, evaluation, and the time discrepancy between predicted and actual task switches. In our work, we examine the use of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">automatically</i> collected computer interaction data for detecting developers’ task switches as well as task types. In two field studies–a 4h observational study and a multi-day study with experience sampling–we collected data from a total of 25 professional developers. Our study results show that we are able to use temporal and semantic features from developers’ computer interaction data to detect task switches and types in the field with high accuracy of 84 percent and 61 percent respectively, and within a short time window of less than 1.6 minutes on average from the actual task switch. We discuss our findings and their practical value for a wide range of applications in real work settings.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| 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