An industrial case study of Coman's automated task detection algorithm: What Worked, What Didn't, and Why
Bibliographic record
Abstract
Programmers need explicit tool support for software maintenance tasks, and a prerequisite for this is an understanding of where the boundaries between distinct tasks lie. Asking developers to indicate manually when they switch tasks is disruptive to their normal work flow, so researchers have sought ways to infer task boundaries automatically based on the content of the interaction histories with the IDE. Coman previously reported a fully automated algorithm that achieved 80% accuracy in a lab validation study. In this paper, we evaluate the use of this algorithm within an industrial setting. We found two problems: first, a large number of the tasks identified are in fact only sessions or subparts of a larger task; second, the demonstrable effects of interruptions are not considered. We argue that the problem of task boundary detection consists of two sub-problems: first, detecting task sessions; and second, linking task sessions. Coman's algorithm only partially addresses the first, and ignores the second.
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How this classification was reachedexpand
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 | 0.000 |
| 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 | 0.002 | 0.015 |
| 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".